dataset_name
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1 value
task
stringclasses
9 values
case
stringclasses
44 values
prompt
stringlengths
799
6.65k
answer
stringlengths
1
486
nodes
stringclasses
23 values
edges
stringlengths
72
991
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samples
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name_to_id_mapping
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119
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source_file
stringclasses
1 value
answer_type
stringclasses
2 values
real_erdos
common_neighbor
collaborative_filtering
An e-commerce platform tracks customer preferences. Customers: Customer_A, User_101, Return_Customer_1, Purchaser_2, Cart_Owner_A, User_102, Buyer_3, Guest_User_1, Shopper_Alpha, Frequent_Buyer_A, User_103, Return_Customer_2, New_Customer_A, VIP_Customer_2, Budget_Buyer_A, VIP_Customer_1, Product_Reviewer_1, Guest_User...
["Frequent_Buyer_A", "Subscriber_Silver"]
(0, 23)
[[0, 2], [0, 5], [1, 9], [1, 13], [1, 16], [1, 22], [2, 7], [2, 9], [2, 11], [2, 14], [2, 18], [2, 21], [2, 22], [3, 5], [3, 6], [3, 13], [4, 9], [4, 11], [4, 15], [4, 18], [4, 19], [5, 7], [5, 10], [5, 11], [5, 15], [5, 20], [5, 22], [6, 9], [6, 16], [6, 17], [7, 8], [7, 12], [7, 13], [7, 15], [8, 15], [8, 16], [9, 12...
undirected
[0, 0]
{"Customer_A": "0", "User_101": "1", "Return_Customer_1": "2", "Purchaser_2": "3", "Cart_Owner_A": "4", "User_102": "5", "Buyer_3": "6", "Guest_User_1": "7", "Shopper_Alpha": "8", "Frequent_Buyer_A": "9", "User_103": "10", "Return_Customer_2": "11", "New_Customer_A": "12", "VIP_Customer_2": "13", "Budget_Buyer_A": "14"...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
To personalize recommendations, examine this customer network: Wishlist_User_2, Premium_Shopper_1, Subscriber_Silver, Loyalty_Member_2, Cart_Owner_A, Regular_Customer_A, Order_Placer_B, Guest_User_1, Consumer_B, Seasonal_Shopper_1, Loyalty_Member_1, Buyer_1, Buyer_2, Purchaser_2, New_Customer_B, Cart_Owner_B, Premium_S...
["Guest_User_1"]
(0, 21)
[[0, 1], [0, 2], [0, 3], [0, 6], [0, 11], [0, 15], [1, 9], [1, 10], [1, 11], [1, 12], [1, 18], [1, 20], [2, 14], [2, 15], [2, 16], [2, 20], [3, 6], [3, 9], [3, 15], [3, 16], [4, 7], [4, 8], [4, 10], [4, 17], [4, 20], [5, 8], [5, 11], [5, 16], [6, 14], [6, 16], [7, 13], [7, 14], [7, 20], [8, 15], [8, 16], [8, 19], [9, 1...
undirected
[0, 0]
{"Wishlist_User_2": "0", "Premium_Shopper_1": "1", "Subscriber_Silver": "2", "Loyalty_Member_2": "3", "Cart_Owner_A": "4", "Regular_Customer_A": "5", "Order_Placer_B": "6", "Guest_User_1": "7", "Consumer_B": "8", "Seasonal_Shopper_1": "9", "Loyalty_Member_1": "10", "Buyer_1": "11", "Buyer_2": "12", "Purchaser_2": "13",...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
To personalize recommendations, examine this customer network: Loyalty_Member_1, Regular_Customer_A, Client_Z, Loyalty_Member_2, Return_Customer_1, Shopper_Beta, Customer_B, Seasonal_Shopper_1, VIP_Customer_1, Product_Reviewer_2, Purchaser_2, Frequent_Buyer_A, Consumer_B, Regular_Customer_B, User_102, Consumer_A, Buyer...
["Loyalty_Member_1"]
(0, 17)
[[0, 1], [0, 3], [1, 13], [1, 14], [3, 6], [4, 6], [4, 8], [4, 11], [5, 10], [6, 7], [6, 8], [6, 11], [7, 15], [9, 10], [9, 11], [9, 12], [10, 14], [12, 13], [13, 15]]
undirected
[0, 0]
{"Loyalty_Member_1": "0", "Regular_Customer_A": "1", "Client_Z": "2", "Loyalty_Member_2": "3", "Return_Customer_1": "4", "Shopper_Beta": "5", "Customer_B": "6", "Seasonal_Shopper_1": "7", "VIP_Customer_1": "8", "Product_Reviewer_2": "9", "Purchaser_2": "10", "Frequent_Buyer_A": "11", "Consumer_B": "12", "Regular_Custom...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
In a recommendation system with users User_101, Buyer_1, Account_1, Premium_Shopper_2, Subscriber_Silver, Consumer_A, New_Customer_B, Client_X, Consumer_B, New_Customer_A, Buyer_2, Order_Placer_A, Guest_User_2, Loyalty_Member_2, Seasonal_Shopper_1, Regular_Customer_A, Client_Y, Member_Plus, Customer_C, Premium_Shopper_...
["User_101", "Customer_C"]
(0, 25)
[[0, 5], [0, 6], [0, 8], [0, 11], [0, 12], [0, 15], [0, 23], [0, 24], [1, 2], [1, 3], [1, 4], [1, 5], [1, 11], [1, 15], [1, 23], [2, 9], [2, 16], [2, 20], [3, 23], [4, 22], [5, 9], [5, 15], [5, 18], [5, 20], [6, 10], [6, 21], [6, 23], [7, 17], [7, 19], [8, 13], [8, 16], [8, 18], [8, 19], [8, 24], [9, 15], [10, 12], [10...
undirected
[0, 0]
{"User_101": "0", "Buyer_1": "1", "Account_1": "2", "Premium_Shopper_2": "3", "Subscriber_Silver": "4", "Consumer_A": "5", "New_Customer_B": "6", "Client_X": "7", "Consumer_B": "8", "New_Customer_A": "9", "Buyer_2": "10", "Order_Placer_A": "11", "Guest_User_2": "12", "Loyalty_Member_2": "13", "Seasonal_Shopper_1": "14"...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
An e-commerce platform tracks customer preferences. Customers: Shopper_Beta, Customer_B, Consumer_B, Loyalty_Member_1, VIP_Customer_2, Frequent_Buyer_A, Purchaser_2, User_101, Member_Prime, Product_Reviewer_1, Client_X, Buyer_1. Purchase history connections: Shopper_Beta and Loyalty_Member_1 share product interests. Sh...
["VIP_Customer_2"]
(0, 12)
[[0, 3], [0, 4], [0, 11], [1, 4], [1, 8], [2, 6], [2, 7], [3, 5], [3, 6], [3, 10], [4, 7], [5, 7], [6, 7], [6, 9], [8, 11], [9, 11]]
undirected
[0, 0]
{"Shopper_Beta": "0", "Customer_B": "1", "Consumer_B": "2", "Loyalty_Member_1": "3", "VIP_Customer_2": "4", "Frequent_Buyer_A": "5", "Purchaser_2": "6", "User_101": "7", "Member_Prime": "8", "Product_Reviewer_1": "9", "Client_X": "10", "Buyer_1": "11"}
none
node_list
real_erdos
common_neighbor
collaborative_filtering
An e-commerce platform tracks customer preferences. Customers: Return_Customer_1, Client_Z, Frequent_Buyer_A, Premium_Shopper_1, Seasonal_Shopper_1, Customer_C, Subscriber_Gold, Client_Y, Loyalty_Member_1, Wishlist_User_2, Buyer_1, Member_Plus, Guest_User_1, Account_1, Frequent_Buyer_B. Purchase history connections: Re...
["Seasonal_Shopper_1"]
(0, 15)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 6], [0, 7], [0, 8], [0, 14], [1, 5], [1, 12], [2, 3], [2, 4], [2, 6], [2, 9], [2, 10], [3, 7], [4, 5], [4, 8], [4, 11], [4, 12], [5, 10], [8, 9], [8, 11], [10, 13], [12, 13], [12, 14]]
undirected
[0, 0]
{"Return_Customer_1": "0", "Client_Z": "1", "Frequent_Buyer_A": "2", "Premium_Shopper_1": "3", "Seasonal_Shopper_1": "4", "Customer_C": "5", "Subscriber_Gold": "6", "Client_Y": "7", "Loyalty_Member_1": "8", "Wishlist_User_2": "9", "Buyer_1": "10", "Member_Plus": "11", "Guest_User_1": "12", "Account_1": "13", "Frequent_...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
For product recommendations, analyze the shopping network: User_101, VIP_Customer_1, Wishlist_User_2, Order_Placer_A, Client_X, Budget_Buyer_A, Frequent_Buyer_A, Return_Customer_1, New_Customer_B, Client_Z, Client_Y, Purchaser_1, Budget_Buyer_B, Buyer_3, Loyalty_Member_1, Loyalty_Member_2, Frequent_Buyer_B, Buyer_1, Us...
["New_Customer_B", "Loyalty_Member_2"]
(0, 19)
[[0, 4], [0, 6], [0, 7], [0, 10], [0, 17], [1, 2], [1, 4], [1, 5], [1, 9], [1, 14], [1, 17], [2, 4], [2, 9], [2, 13], [2, 14], [3, 8], [3, 13], [3, 15], [3, 17], [4, 8], [4, 10], [4, 14], [4, 17], [5, 6], [5, 8], [5, 11], [5, 13], [5, 15], [5, 18], [6, 8], [6, 9], [6, 14], [6, 15], [6, 17], [7, 9], [7, 12], [7, 13], [7...
undirected
[0, 0]
{"User_101": "0", "VIP_Customer_1": "1", "Wishlist_User_2": "2", "Order_Placer_A": "3", "Client_X": "4", "Budget_Buyer_A": "5", "Frequent_Buyer_A": "6", "Return_Customer_1": "7", "New_Customer_B": "8", "Client_Z": "9", "Client_Y": "10", "Purchaser_1": "11", "Budget_Buyer_B": "12", "Buyer_3": "13", "Loyalty_Member_1": "...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
In a recommendation system with users Subscriber_Gold, Customer_A, Consumer_A, Wishlist_User_1, Account_2, Budget_Buyer_B, Premium_Shopper_1, Purchaser_1, User_103, Cart_Owner_B, Return_Customer_2, User_102, Shopper_Alpha and preference links Subscriber_Gold bought products also purchased by Customer_A. Subscriber_Gold...
["Subscriber_Gold", "Premium_Shopper_1", "Purchaser_1"]
(0, 13)
[[0, 1], [0, 7], [0, 8], [0, 10], [0, 11], [1, 2], [1, 3], [1, 4], [1, 6], [1, 7], [1, 9], [2, 9], [3, 6], [3, 10], [3, 12], [4, 5], [4, 6], [4, 9], [4, 12], [5, 10], [5, 12], [6, 11], [6, 12], [7, 9], [7, 10], [7, 11], [8, 11], [8, 12], [9, 10], [10, 11]]
undirected
[0, 0]
{"Subscriber_Gold": "0", "Customer_A": "1", "Consumer_A": "2", "Wishlist_User_1": "3", "Account_2": "4", "Budget_Buyer_B": "5", "Premium_Shopper_1": "6", "Purchaser_1": "7", "User_103": "8", "Cart_Owner_B": "9", "Return_Customer_2": "10", "User_102": "11", "Shopper_Alpha": "12"}
none
node_list
real_erdos
common_neighbor
collaborative_filtering
For product recommendations, analyze the shopping network: Buyer_3, Cart_Owner_A, Return_Customer_2, Client_Y, Consumer_B, Regular_Customer_B, Return_Customer_1, Budget_Buyer_B, Shopper_Alpha, Customer_C, New_Customer_B, User_101, Budget_Buyer_A, VIP_Customer_1, Product_Reviewer_1, Premium_Shopper_2, Seasonal_Shopper_1...
["New_Customer_B"]
(0, 22)
[[0, 2], [0, 3], [0, 5], [0, 6], [0, 11], [0, 12], [0, 17], [0, 20], [0, 21], [1, 6], [1, 7], [1, 8], [1, 9], [1, 13], [1, 16], [1, 19], [2, 6], [2, 8], [2, 18], [2, 20], [3, 7], [3, 10], [3, 14], [3, 18], [3, 19], [3, 21], [4, 8], [4, 9], [4, 11], [4, 14], [4, 16], [4, 17], [4, 20], [5, 10], [5, 11], [5, 14], [5, 17],...
undirected
[0, 0]
{"Buyer_3": "0", "Cart_Owner_A": "1", "Return_Customer_2": "2", "Client_Y": "3", "Consumer_B": "4", "Regular_Customer_B": "5", "Return_Customer_1": "6", "Budget_Buyer_B": "7", "Shopper_Alpha": "8", "Customer_C": "9", "New_Customer_B": "10", "User_101": "11", "Budget_Buyer_A": "12", "VIP_Customer_1": "13", "Product_Revi...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
To personalize recommendations, examine this customer network: VIP_Customer_1, Order_Placer_B, Wishlist_User_1, Account_1, Buyer_3, Budget_Buyer_A, Return_Customer_1, Product_Reviewer_2, Order_Placer_A, Buyer_2, New_Customer_B, Return_Customer_2, User_102, Frequent_Buyer_B, Member_Prime, Subscriber_Gold, Account_2, Gue...
["Buyer_3"]
(0, 25)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 6], [1, 4], [1, 8], [1, 11], [1, 16], [2, 5], [2, 7], [2, 9], [2, 10], [2, 11], [2, 12], [2, 13], [2, 14], [2, 19], [3, 4], [3, 5], [3, 6], [3, 7], [3, 8], [3, 9], [3, 11], [3, 13], [3, 15], [3, 20], [3, 21], [4, 5], [4, 9], [4, 14], [4, 18], [4, 24], [5, 6], [5, 7], [6, 10], [6, 13...
undirected
[0, 0]
{"VIP_Customer_1": "0", "Order_Placer_B": "1", "Wishlist_User_1": "2", "Account_1": "3", "Buyer_3": "4", "Budget_Buyer_A": "5", "Return_Customer_1": "6", "Product_Reviewer_2": "7", "Order_Placer_A": "8", "Buyer_2": "9", "New_Customer_B": "10", "Return_Customer_2": "11", "User_102": "12", "Frequent_Buyer_B": "13", "Memb...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
An e-commerce platform tracks customer preferences. Customers: Seasonal_Shopper_1, Client_X, Budget_Buyer_A, Buyer_3, Guest_User_2, Member_Prime, Regular_Customer_A, User_101, Purchaser_1, Subscriber_Gold, Buyer_1, VIP_Customer_1, Wishlist_User_2, Shopper_Alpha, Frequent_Buyer_B, New_Customer_A, Cart_Owner_A, Product_R...
["Seasonal_Shopper_1"]
(0, 18)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 7], [0, 8], [0, 9], [0, 10], [0, 11], [0, 12], [0, 13], [0, 14], [0, 16], [0, 17], [1, 4], [1, 5], [1, 6], [1, 14], [2, 3], [2, 10], [2, 16], [3, 11], [3, 13], [3, 15], [3, 17], [4, 6], [4, 8], [4, 9], [4, 12], [6, 7], [12, 15]]
undirected
[0, 0]
{"Seasonal_Shopper_1": "0", "Client_X": "1", "Budget_Buyer_A": "2", "Buyer_3": "3", "Guest_User_2": "4", "Member_Prime": "5", "Regular_Customer_A": "6", "User_101": "7", "Purchaser_1": "8", "Subscriber_Gold": "9", "Buyer_1": "10", "VIP_Customer_1": "11", "Wishlist_User_2": "12", "Shopper_Alpha": "13", "Frequent_Buyer_B...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
To personalize recommendations, examine this customer network: Client_Y, Buyer_2, Loyalty_Member_2, Order_Placer_B, Account_1, Purchaser_1, Guest_User_1, Subscriber_Gold, Shopper_Alpha, VIP_Customer_1, User_103, Wishlist_User_2, Premium_Shopper_2, Member_Plus, New_Customer_B, Consumer_B, Cart_Owner_B, Order_Placer_A, B...
["Loyalty_Member_2", "Shopper_Alpha"]
(0, 24)
[[0, 6], [0, 9], [0, 10], [0, 16], [0, 17], [1, 8], [1, 9], [1, 13], [1, 14], [1, 15], [1, 16], [1, 20], [1, 21], [1, 22], [2, 3], [2, 7], [2, 11], [2, 13], [2, 14], [2, 21], [3, 6], [3, 8], [3, 9], [3, 13], [3, 15], [3, 17], [3, 18], [3, 22], [3, 23], [4, 9], [4, 13], [4, 16], [4, 17], [4, 22], [4, 23], [5, 7], [5, 9]...
undirected
[0, 0]
{"Client_Y": "0", "Buyer_2": "1", "Loyalty_Member_2": "2", "Order_Placer_B": "3", "Account_1": "4", "Purchaser_1": "5", "Guest_User_1": "6", "Subscriber_Gold": "7", "Shopper_Alpha": "8", "VIP_Customer_1": "9", "User_103": "10", "Wishlist_User_2": "11", "Premium_Shopper_2": "12", "Member_Plus": "13", "New_Customer_B": "...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
To personalize recommendations, examine this customer network: Customer_C, Subscriber_Gold, VIP_Customer_1, Guest_User_2, Member_Plus, Customer_B, Cart_Owner_B, Purchaser_1, Regular_Customer_A, Return_Customer_1, Budget_Buyer_A, Return_Customer_2, Client_Y, Buyer_1, Frequent_Buyer_B, Guest_User_1, Consumer_B, Premium_S...
["Premium_Shopper_2"]
(0, 19)
[[0, 3], [0, 6], [0, 7], [0, 8], [0, 9], [0, 10], [0, 13], [0, 16], [0, 18], [1, 2], [1, 4], [1, 7], [1, 9], [1, 11], [1, 12], [1, 15], [2, 5], [2, 15], [2, 17], [3, 9], [4, 7], [4, 8], [4, 16], [5, 7], [5, 9], [5, 10], [5, 17], [6, 7], [6, 9], [6, 17], [7, 17], [8, 12], [8, 16], [8, 17], [9, 10], [9, 13], [9, 17], [9,...
undirected
[0, 0]
{"Customer_C": "0", "Subscriber_Gold": "1", "VIP_Customer_1": "2", "Guest_User_2": "3", "Member_Plus": "4", "Customer_B": "5", "Cart_Owner_B": "6", "Purchaser_1": "7", "Regular_Customer_A": "8", "Return_Customer_1": "9", "Budget_Buyer_A": "10", "Return_Customer_2": "11", "Client_Y": "12", "Buyer_1": "13", "Frequent_Buy...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
To personalize recommendations, examine this customer network: Purchaser_2, Shopper_Beta, Regular_Customer_B, Budget_Buyer_B, Buyer_3, Cart_Owner_A, Regular_Customer_A, Order_Placer_A, User_103, Account_2, Client_Z, Product_Reviewer_1, Wishlist_User_1, Client_Y, VIP_Customer_1, User_102, Return_Customer_2, Account_1, B...
["Cart_Owner_A"]
(0, 21)
[[0, 7], [0, 12], [0, 15], [0, 17], [1, 4], [1, 6], [1, 19], [2, 6], [2, 8], [2, 12], [2, 13], [2, 20], [3, 5], [3, 11], [3, 16], [3, 17], [4, 7], [4, 8], [4, 10], [4, 16], [5, 7], [5, 8], [5, 10], [6, 9], [6, 10], [6, 11], [6, 15], [6, 16], [6, 17], [7, 12], [7, 14], [7, 15], [7, 17], [7, 18], [7, 20], [8, 11], [8, 13...
undirected
[0, 0]
{"Purchaser_2": "0", "Shopper_Beta": "1", "Regular_Customer_B": "2", "Budget_Buyer_B": "3", "Buyer_3": "4", "Cart_Owner_A": "5", "Regular_Customer_A": "6", "Order_Placer_A": "7", "User_103": "8", "Account_2": "9", "Client_Z": "10", "Product_Reviewer_1": "11", "Wishlist_User_1": "12", "Client_Y": "13", "VIP_Customer_1":...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
An e-commerce platform tracks customer preferences. Customers: Product_Reviewer_2, Consumer_A, Budget_Buyer_A, New_Customer_B, Buyer_1, Customer_A, Customer_C, User_101, Order_Placer_B, Loyalty_Member_2, Guest_User_1, Product_Reviewer_1, Guest_User_2, Account_2, Premium_Shopper_2, Account_1, User_103, Shopper_Beta, Cli...
["Customer_A"]
(0, 20)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 7], [2, 4], [2, 6], [2, 8], [2, 9], [2, 12], [3, 4], [3, 5], [3, 6], [3, 7], [3, 8], [3, 9], [3, 10], [3, 11], [3, 13], [3, 16], [4, 5], [4, 14], [4, 18], [4, 19], [5, 6], [5, 7], [5, 8], [5, 9], [5, 10], [5, 12], [5, 14], [5, 15], [5, 16], [5, 17], [5, 18], [7, 16], [8, 11]...
undirected
[0, 0]
{"Product_Reviewer_2": "0", "Consumer_A": "1", "Budget_Buyer_A": "2", "New_Customer_B": "3", "Buyer_1": "4", "Customer_A": "5", "Customer_C": "6", "User_101": "7", "Order_Placer_B": "8", "Loyalty_Member_2": "9", "Guest_User_1": "10", "Product_Reviewer_1": "11", "Guest_User_2": "12", "Account_2": "13", "Premium_Shopper_...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
In a recommendation system with users Subscriber_Silver, Buyer_3, Order_Placer_A, Customer_A, Wishlist_User_2, Wishlist_User_1, Client_Z, Purchaser_1, Return_Customer_1, Purchaser_2, Loyalty_Member_2, Buyer_1 and preference links Subscriber_Silver and Wishlist_User_2 share product interests. Subscriber_Silver and Buyer...
["Loyalty_Member_2"]
(0, 12)
[[0, 4], [0, 11], [1, 9], [1, 10], [1, 11], [2, 6], [2, 7], [3, 4], [3, 11], [4, 7], [6, 9], [6, 10], [9, 10]]
undirected
[0, 0]
{"Subscriber_Silver": "0", "Buyer_3": "1", "Order_Placer_A": "2", "Customer_A": "3", "Wishlist_User_2": "4", "Wishlist_User_1": "5", "Client_Z": "6", "Purchaser_1": "7", "Return_Customer_1": "8", "Purchaser_2": "9", "Loyalty_Member_2": "10", "Buyer_1": "11"}
none
node_list
real_erdos
common_neighbor
collaborative_filtering
To personalize recommendations, examine this customer network: Customer_B, User_103, Consumer_A, Regular_Customer_B, Account_1, Guest_User_1, Loyalty_Member_2, Shopper_Beta, Member_Plus, Client_X, Customer_A, Account_2, Cart_Owner_B, Subscriber_Silver, Member_Prime with interactions: Customer_B has shopping patterns si...
["Customer_B", "Loyalty_Member_2"]
(0, 15)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 6], [0, 12], [1, 4], [1, 6], [1, 9], [1, 10], [1, 11], [1, 14], [2, 3], [2, 5], [2, 9], [2, 11], [2, 13], [3, 7], [3, 8], [4, 5], [6, 7], [6, 8], [6, 10], [6, 12], [6, 13], [6, 14]]
undirected
[0, 0]
{"Customer_B": "0", "User_103": "1", "Consumer_A": "2", "Regular_Customer_B": "3", "Account_1": "4", "Guest_User_1": "5", "Loyalty_Member_2": "6", "Shopper_Beta": "7", "Member_Plus": "8", "Client_X": "9", "Customer_A": "10", "Account_2": "11", "Cart_Owner_B": "12", "Subscriber_Silver": "13", "Member_Prime": "14"}
none
node_list
real_erdos
common_neighbor
collaborative_filtering
An e-commerce platform tracks customer preferences. Customers: Budget_Buyer_B, Budget_Buyer_A, VIP_Customer_2, Frequent_Buyer_B, Client_Y, Consumer_B, Regular_Customer_A, Buyer_1, Return_Customer_1, Member_Plus, Loyalty_Member_2, Product_Reviewer_2, Guest_User_2, Premium_Shopper_1, Client_Z, Loyalty_Member_1. Purchase ...
["Budget_Buyer_B", "Premium_Shopper_1"]
(0, 16)
[[0, 1], [0, 2], [0, 3], [0, 7], [0, 10], [0, 11], [2, 3], [2, 4], [2, 8], [2, 9], [2, 12], [2, 14], [2, 15], [3, 4], [3, 5], [3, 11], [3, 13], [4, 5], [4, 6], [4, 8], [4, 9], [5, 6], [6, 7], [6, 14], [6, 15], [7, 10], [7, 13], [11, 12]]
undirected
[0, 0]
{"Budget_Buyer_B": "0", "Budget_Buyer_A": "1", "VIP_Customer_2": "2", "Frequent_Buyer_B": "3", "Client_Y": "4", "Consumer_B": "5", "Regular_Customer_A": "6", "Buyer_1": "7", "Return_Customer_1": "8", "Member_Plus": "9", "Loyalty_Member_2": "10", "Product_Reviewer_2": "11", "Guest_User_2": "12", "Premium_Shopper_1": "13...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
In a recommendation system with users Cart_Owner_A, Shopper_Alpha, Cart_Owner_B, Regular_Customer_A, Loyalty_Member_1, Order_Placer_B, Premium_Shopper_2, Product_Reviewer_1, Wishlist_User_2, User_102, VIP_Customer_2, Consumer_A, Account_1, Guest_User_1, Subscriber_Gold, Customer_B, VIP_Customer_1 and preference links C...
["Cart_Owner_A", "Order_Placer_B", "Premium_Shopper_2", "VIP_Customer_2", "Subscriber_Gold"]
(0, 17)
[[0, 1], [0, 6], [0, 7], [0, 9], [0, 13], [0, 15], [1, 5], [1, 10], [1, 11], [1, 13], [2, 3], [2, 4], [2, 6], [2, 10], [2, 11], [2, 13], [2, 14], [3, 4], [3, 9], [3, 11], [3, 13], [3, 14], [3, 15], [4, 6], [4, 11], [4, 12], [4, 16], [5, 6], [5, 7], [5, 10], [5, 11], [5, 15], [5, 16], [6, 7], [6, 9], [6, 13], [6, 15], [...
undirected
[0, 0]
{"Cart_Owner_A": "0", "Shopper_Alpha": "1", "Cart_Owner_B": "2", "Regular_Customer_A": "3", "Loyalty_Member_1": "4", "Order_Placer_B": "5", "Premium_Shopper_2": "6", "Product_Reviewer_1": "7", "Wishlist_User_2": "8", "User_102": "9", "VIP_Customer_2": "10", "Consumer_A": "11", "Account_1": "12", "Guest_User_1": "13", "...
none
node_list
real_erdos
common_neighbor
collaborative_filtering
In a recommendation system with users Seasonal_Shopper_1, Member_Prime, Wishlist_User_2, Guest_User_2, Cart_Owner_B, Product_Reviewer_1, Frequent_Buyer_B, Order_Placer_A, Consumer_A, Frequent_Buyer_A, Member_Plus and preference links There is a preference overlap between Seasonal_Shopper_1 and Consumer_A. Member_Prime ...
["Order_Placer_A", "Frequent_Buyer_A"]
(0, 11)
[[0, 8], [1, 3], [1, 5], [1, 8], [2, 10], [3, 6], [3, 7], [3, 9], [4, 5], [4, 7], [4, 9], [5, 8], [5, 10], [6, 7], [6, 9], [7, 9], [7, 10]]
undirected
[0, 0]
{"Seasonal_Shopper_1": "0", "Member_Prime": "1", "Wishlist_User_2": "2", "Guest_User_2": "3", "Cart_Owner_B": "4", "Product_Reviewer_1": "5", "Frequent_Buyer_B": "6", "Order_Placer_A": "7", "Consumer_A": "8", "Frequent_Buyer_A": "9", "Member_Plus": "10"}
none
node_list
real_erdos
common_neighbor
friend_recommendation
A social media platform is analyzing user connections. Users include: Steve, Profile_C, Jack, Member_1, Wendy, Alice, Isabel, Profile_B, Diana, User_Alpha, Subscriber_1, Subscriber_2, Zack, Henry, Xavier, Follower_A, Community_Member_B, Yara, Grace, Leo, Charlie, User_Gamma, Contact_Y. Current connections: Steve has Me...
["Wendy", "Alice", "Leo", "Charlie"]
(0, 23)
[[0, 3], [0, 6], [0, 9], [0, 10], [0, 13], [0, 17], [0, 20], [0, 21], [1, 3], [1, 8], [1, 12], [1, 13], [1, 14], [1, 15], [1, 16], [1, 17], [1, 21], [1, 22], [2, 4], [2, 8], [2, 15], [2, 18], [2, 19], [2, 20], [3, 6], [3, 7], [3, 9], [3, 13], [3, 16], [3, 20], [4, 5], [4, 7], [4, 10], [4, 13], [4, 17], [5, 7], [5, 8], ...
undirected
[0, 0]
{"Steve": "0", "Profile_C": "1", "Jack": "2", "Member_1": "3", "Wendy": "4", "Alice": "5", "Isabel": "6", "Profile_B": "7", "Diana": "8", "User_Alpha": "9", "Subscriber_1": "10", "Subscriber_2": "11", "Zack": "12", "Henry": "13", "Xavier": "14", "Follower_A": "15", "Community_Member_B": "16", "Yara": "17", "Grace": "18...
none
node_list
real_erdos
common_neighbor
friend_recommendation
A social media platform is analyzing user connections. Users include: Grace, Henry, Steve, Nathan, Subscriber_1, Follower_A, Friend_3, Profile_C, Contact_X, User_Beta, Isabel, Follower_B, Maria, Kate, Jack, Community_Member_A, Friend_2, Contact_Z, Network_User_2, Frank, Member_1, Charlie, Leo. Current connections: Grac...
["Friend_3", "Contact_X"]
(0, 23)
[[0, 1], [0, 4], [0, 6], [0, 8], [0, 15], [0, 19], [1, 3], [1, 6], [1, 9], [1, 14], [1, 20], [1, 21], [2, 8], [2, 10], [2, 13], [2, 19], [2, 20], [2, 22], [3, 5], [3, 10], [4, 5], [4, 13], [4, 14], [4, 19], [4, 21], [4, 22], [5, 16], [5, 22], [6, 8], [6, 9], [6, 15], [6, 16], [6, 17], [6, 21], [7, 8], [7, 10], [7, 13],...
undirected
[0, 0]
{"Grace": "0", "Henry": "1", "Steve": "2", "Nathan": "3", "Subscriber_1": "4", "Follower_A": "5", "Friend_3": "6", "Profile_C": "7", "Contact_X": "8", "User_Beta": "9", "Isabel": "10", "Follower_B": "11", "Maria": "12", "Kate": "13", "Jack": "14", "Community_Member_A": "15", "Friend_2": "16", "Contact_Z": "17", "Networ...
none
node_list
real_erdos
common_neighbor
friend_recommendation
A social media platform is analyzing user connections. Users include: Isabel, Zack, User_Beta, Friend_1, Follower_B, Xavier, Leo, Grace, Maria, Uma, Friend_2, Community_Member_A, Frank, Contact_Y, Network_User_2, Profile_B, Victor, Member_1, Yara. Current connections: There is a friendship between Isabel and Zack. Ther...
["Zack", "Follower_B", "Xavier", "Network_User_2"]
(0, 19)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 7], [0, 8], [0, 9], [0, 10], [0, 12], [0, 13], [0, 14], [0, 15], [0, 16], [0, 18], [1, 4], [1, 5], [1, 6], [1, 7], [1, 16], [1, 17], [2, 4], [2, 17], [3, 14], [4, 5], [4, 6], [4, 7], [4, 8], [4, 9], [4, 10], [4, 11], [4, 13], [5, 6], [6, 14], [7, 8], [7, 9], [7, 11], [7, 12]...
undirected
[0, 0]
{"Isabel": "0", "Zack": "1", "User_Beta": "2", "Friend_1": "3", "Follower_B": "4", "Xavier": "5", "Leo": "6", "Grace": "7", "Maria": "8", "Uma": "9", "Friend_2": "10", "Community_Member_A": "11", "Frank": "12", "Contact_Y": "13", "Network_User_2": "14", "Profile_B": "15", "Victor": "16", "Member_1": "17", "Yara": "18"}
none
node_list
real_erdos
common_neighbor
friend_recommendation
Given a friendship network with members Diana, Quinn, Profile_B, Yara, Subscriber_2, Isabel, User_Delta, Friend_2, Leo, Uma, Peter, Victor, Follower_A, Community_Member_B and relationships Diana and Subscriber_2 are connected. Diana has Isabel in their social circle. Diana and Friend_2 are connected. There is a friends...
["Isabel"]
(0, 14)
[[0, 4], [0, 5], [0, 7], [0, 9], [0, 10], [1, 3], [1, 4], [1, 6], [1, 7], [1, 10], [1, 11], [2, 5], [3, 5], [3, 8], [3, 9], [3, 11], [3, 12], [4, 8], [4, 9], [5, 6], [5, 8], [5, 10], [5, 12], [5, 13], [7, 13], [8, 10], [9, 10], [9, 11], [10, 13], [11, 13]]
undirected
[0, 0]
{"Diana": "0", "Quinn": "1", "Profile_B": "2", "Yara": "3", "Subscriber_2": "4", "Isabel": "5", "User_Delta": "6", "Friend_2": "7", "Leo": "8", "Uma": "9", "Peter": "10", "Victor": "11", "Follower_A": "12", "Community_Member_B": "13"}
none
node_list
real_erdos
common_neighbor
friend_recommendation
A social media platform is analyzing user connections. Users include: Maria, Contact_Z, Olivia, Diana, Friend_1, Network_User_1, User_Gamma, Xavier, Friend_3, Leo, Subscriber_2, User_Delta, Tina, Quinn, Yara, Isabel, Member_2, Community_Member_B, Rachel, Charlie, Community_Member_A, Peter, Jack, Member_3. Current conne...
["Network_User_1"]
(0, 24)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 9], [0, 13], [0, 14], [0, 19], [1, 4], [1, 5], [1, 9], [1, 12], [1, 13], [1, 14], [1, 16], [1, 21], [2, 5], [2, 7], [2, 10], [2, 12], [3, 4], [3, 5], [3, 6], [3, 8], [3, 11], [3, 12], [3, 13], [3, 17], [3, 18], [3, 20], [4, 6], [4, 7], [4, 8], [4, 9], [4, 10], [4, 11], [4, 15], [4, ...
undirected
[0, 0]
{"Maria": "0", "Contact_Z": "1", "Olivia": "2", "Diana": "3", "Friend_1": "4", "Network_User_1": "5", "User_Gamma": "6", "Xavier": "7", "Friend_3": "8", "Leo": "9", "Subscriber_2": "10", "User_Delta": "11", "Tina": "12", "Quinn": "13", "Yara": "14", "Isabel": "15", "Member_2": "16", "Community_Member_B": "17", "Rachel"...
none
node_list
real_erdos
common_neighbor
friend_recommendation
To improve friend suggestions, we're examining the network: Profile_B, Charlie, Member_1, Friend_3, Alice, Member_2, Yara, Subscriber_1, User_Delta, Frank, Jack, Contact_Y, Community_Member_A, Isabel, Nathan. Friendship links: There is a friendship between Profile_B and Charlie. There is a friendship between Profile_B ...
["Charlie"]
(0, 15)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 9], [0, 10], [0, 12], [0, 13], [0, 14], [1, 3], [1, 5], [1, 7], [1, 10], [1, 12], [2, 4], [2, 6], [2, 7], [2, 8], [2, 14], [4, 9], [4, 11], [5, 13], [6, 8], [7, 11]]
undirected
[0, 0]
{"Profile_B": "0", "Charlie": "1", "Member_1": "2", "Friend_3": "3", "Alice": "4", "Member_2": "5", "Yara": "6", "Subscriber_1": "7", "User_Delta": "8", "Frank": "9", "Jack": "10", "Contact_Y": "11", "Community_Member_A": "12", "Isabel": "13", "Nathan": "14"}
none
node_list
real_erdos
common_neighbor
friend_recommendation
To improve friend suggestions, we're examining the network: User_Beta, Peter, Diana, Alice, Follower_B, Isabel, Maria, Follower_A, Profile_C, Tina, Yara, Nathan, Grace, Community_Member_B, Steve, Leo, Subscriber_1, Subscriber_2, User_Gamma, Uma. Friendship links: There is a friendship between User_Beta and Peter. User_...
["Profile_C"]
(0, 20)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 9], [0, 12], [0, 14], [0, 15], [0, 17], [2, 4], [2, 5], [2, 6], [2, 8], [2, 13], [3, 4], [3, 6], [3, 7], [3, 11], [3, 12], [3, 16], [3, 18], [3, 19], [4, 5], [4, 7], [4, 8], [4, 9], [4, 10], [4, 12], [4, 13], [4, 14], [4, 17], [5, 8], [5, 10], [5, 11], [5, 15], [5, 1...
undirected
[0, 0]
{"User_Beta": "0", "Peter": "1", "Diana": "2", "Alice": "3", "Follower_B": "4", "Isabel": "5", "Maria": "6", "Follower_A": "7", "Profile_C": "8", "Tina": "9", "Yara": "10", "Nathan": "11", "Grace": "12", "Community_Member_B": "13", "Steve": "14", "Leo": "15", "Subscriber_1": "16", "Subscriber_2": "17", "User_Gamma": "1...
none
node_list
real_erdos
common_neighbor
friend_recommendation
In a social network with users Peter, Kate, Tina, Leo, Diana, Uma, Zack, Profile_A, Jack, Subscriber_1, Steve, Quinn, User_Alpha, Follower_A, Friend_2, Friend_1, Emma, Bob, Friend_3, Rachel, Charlie, Member_3, Profile_C, Olivia, we want to recommend potential friends. The existing friendships are: Peter is friends with...
["Follower_A"]
(0, 24)
[[0, 6], [0, 16], [0, 20], [1, 5], [1, 6], [1, 7], [1, 8], [1, 9], [1, 12], [1, 15], [1, 18], [2, 8], [2, 10], [2, 12], [2, 14], [2, 16], [2, 19], [2, 21], [2, 23], [3, 4], [3, 6], [3, 10], [3, 12], [3, 14], [3, 15], [3, 17], [3, 23], [4, 7], [4, 11], [4, 16], [4, 23], [5, 6], [5, 8], [5, 11], [5, 13], [5, 15], [5, 21]...
undirected
[0, 0]
{"Peter": "0", "Kate": "1", "Tina": "2", "Leo": "3", "Diana": "4", "Uma": "5", "Zack": "6", "Profile_A": "7", "Jack": "8", "Subscriber_1": "9", "Steve": "10", "Quinn": "11", "User_Alpha": "12", "Follower_A": "13", "Friend_2": "14", "Friend_1": "15", "Emma": "16", "Bob": "17", "Friend_3": "18", "Rachel": "19", "Charlie"...
none
node_list
real_erdos
common_neighbor
friend_recommendation
A social media platform is analyzing user connections. Users include: Contact_X, User_Alpha, Olivia, Community_Member_A, Friend_3, Charlie, Yara, Xavier, Leo, Member_3, Community_Member_B, Zack, User_Beta. Current connections: Contact_X has Leo in their social circle. User_Alpha has Xavier in their social circle. User_...
["Leo"]
(0, 13)
[[0, 8], [1, 7], [1, 8], [1, 10], [1, 12], [2, 7], [3, 6], [3, 9], [3, 12], [4, 7], [5, 11], [7, 9], [7, 10], [10, 11]]
undirected
[0, 0]
{"Contact_X": "0", "User_Alpha": "1", "Olivia": "2", "Community_Member_A": "3", "Friend_3": "4", "Charlie": "5", "Yara": "6", "Xavier": "7", "Leo": "8", "Member_3": "9", "Community_Member_B": "10", "Zack": "11", "User_Beta": "12"}
none
node_list
real_erdos
common_neighbor
friend_recommendation
In a social network with users User_Delta, Subscriber_1, Bob, Steve, Rachel, Network_User_2, Contact_X, Maria, Member_1, Member_3, Friend_3, Kate, Yara, Community_Member_A, Profile_B, Jack, Peter, User_Beta, Nathan, Community_Member_B, we want to recommend potential friends. The existing friendships are: User_Delta is ...
["Member_3"]
(0, 20)
[[0, 1], [0, 2], [0, 3], [0, 5], [0, 6], [0, 8], [0, 11], [0, 12], [0, 16], [0, 19], [1, 6], [1, 7], [1, 8], [1, 10], [1, 12], [1, 17], [1, 18], [2, 3], [2, 4], [2, 5], [2, 10], [2, 13], [3, 4], [3, 13], [3, 17], [3, 18], [4, 7], [4, 9], [4, 11], [4, 15], [6, 9], [9, 14], [11, 15], [13, 14], [15, 16], [17, 19]]
undirected
[0, 0]
{"User_Delta": "0", "Subscriber_1": "1", "Bob": "2", "Steve": "3", "Rachel": "4", "Network_User_2": "5", "Contact_X": "6", "Maria": "7", "Member_1": "8", "Member_3": "9", "Friend_3": "10", "Kate": "11", "Yara": "12", "Community_Member_A": "13", "Profile_B": "14", "Jack": "15", "Peter": "16", "User_Beta": "17", "Nathan"...
none
node_list
real_erdos
common_neighbor
friend_recommendation
To improve friend suggestions, we're examining the network: Emma, Tina, Isabel, Subscriber_2, Olivia, Henry, Xavier, Follower_A, Rachel, Friend_1, User_Delta, Contact_Z, Zack, Profile_C, User_Beta, Profile_B, Community_Member_A. Friendship links: Emma and Tina are connected. Emma follows Isabel. Tina and Friend_1 are c...
["User_Beta"]
(0, 17)
[[0, 1], [0, 2], [1, 9], [1, 14], [2, 3], [2, 4], [2, 7], [2, 8], [2, 9], [2, 10], [2, 11], [2, 14], [3, 11], [3, 14], [3, 16], [4, 6], [4, 11], [4, 12], [4, 13], [5, 9], [6, 7], [6, 10], [6, 12], [6, 13], [6, 16], [7, 12], [7, 13], [8, 14], [8, 15], [10, 11], [10, 12], [10, 16], [11, 13], [11, 14], [12, 16], [13, 15],...
undirected
[0, 0]
{"Emma": "0", "Tina": "1", "Isabel": "2", "Subscriber_2": "3", "Olivia": "4", "Henry": "5", "Xavier": "6", "Follower_A": "7", "Rachel": "8", "Friend_1": "9", "User_Delta": "10", "Contact_Z": "11", "Zack": "12", "Profile_C": "13", "User_Beta": "14", "Profile_B": "15", "Community_Member_A": "16"}
none
node_list
real_erdos
common_neighbor
friend_recommendation
To improve friend suggestions, we're examining the network: Yara, Friend_2, Nathan, Leo, Xavier, Follower_B, Victor, Jack, Peter, Friend_1, Kate, Frank, Emma, Member_2, Community_Member_A, Charlie, Follower_A, Profile_A, Network_User_2, Olivia, Contact_X, User_Gamma. Friendship links: Yara has Friend_2 in their social ...
["Yara"]
(0, 22)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 6], [0, 7], [0, 9], [0, 12], [0, 17], [0, 19], [0, 21], [2, 3], [2, 4], [2, 5], [2, 7], [2, 9], [2, 18], [3, 10], [3, 11], [3, 12], [3, 15], [3, 16], [3, 18], [3, 19], [3, 20], [4, 5], [4, 6], [4, 8], [4, 11], [4, 14], [4, 15], [5, 20], [5, 21], [6, 8], [6, 10], [8, 13], [8, 14], [9...
undirected
[0, 0]
{"Yara": "0", "Friend_2": "1", "Nathan": "2", "Leo": "3", "Xavier": "4", "Follower_B": "5", "Victor": "6", "Jack": "7", "Peter": "8", "Friend_1": "9", "Kate": "10", "Frank": "11", "Emma": "12", "Member_2": "13", "Community_Member_A": "14", "Charlie": "15", "Follower_A": "16", "Profile_A": "17", "Network_User_2": "18", ...
none
node_list
real_erdos
common_neighbor
friend_recommendation
Given a friendship network with members Rachel, Follower_A, Network_User_1, Isabel, Member_1, Emma, User_Alpha, Uma, Follower_B, Kate, User_Delta, Leo, Member_2, Grace, Xavier, Subscriber_2 and relationships There is a friendship between Rachel and Follower_A. Rachel has Network_User_1 in their social circle. Rachel an...
["Rachel"]
(0, 16)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 10], [0, 11], [1, 3], [1, 5], [1, 9], [1, 14], [1, 15], [3, 4], [3, 12], [4, 8], [4, 11], [5, 6], [5, 7], [5, 8], [6, 10], [6, 15], [7, 9], [8, 12], [9, 13], [11, 13], [13, 14]]
undirected
[0, 0]
{"Rachel": "0", "Follower_A": "1", "Network_User_1": "2", "Isabel": "3", "Member_1": "4", "Emma": "5", "User_Alpha": "6", "Uma": "7", "Follower_B": "8", "Kate": "9", "User_Delta": "10", "Leo": "11", "Member_2": "12", "Grace": "13", "Xavier": "14", "Subscriber_2": "15"}
none
node_list
real_erdos
common_neighbor
friend_recommendation
To improve friend suggestions, we're examining the network: Wendy, Frank, User_Beta, Subscriber_1, Subscriber_2, Grace, Profile_A, Steve, Olivia, Zack, Profile_B, Network_User_1, User_Alpha, Victor, Diana, Isabel, Peter, Maria, Member_3, Member_2, Yara, Follower_B, Community_Member_B, Friend_3. Friendship links: Wendy ...
["Member_2"]
(0, 24)
[[0, 1], [0, 3], [0, 12], [0, 14], [0, 21], [0, 23], [1, 9], [1, 12], [1, 14], [1, 17], [1, 20], [1, 22], [1, 23], [2, 14], [2, 22], [3, 5], [3, 6], [3, 9], [3, 12], [3, 18], [3, 19], [3, 23], [4, 10], [4, 12], [4, 17], [4, 18], [4, 19], [4, 21], [5, 7], [5, 8], [5, 12], [5, 13], [5, 14], [5, 15], [5, 18], [5, 20], [5,...
undirected
[0, 0]
{"Wendy": "0", "Frank": "1", "User_Beta": "2", "Subscriber_1": "3", "Subscriber_2": "4", "Grace": "5", "Profile_A": "6", "Steve": "7", "Olivia": "8", "Zack": "9", "Profile_B": "10", "Network_User_1": "11", "User_Alpha": "12", "Victor": "13", "Diana": "14", "Isabel": "15", "Peter": "16", "Maria": "17", "Member_3": "18",...
none
node_list
real_erdos
common_neighbor
friend_recommendation
In a social network with users Contact_X, User_Beta, Zack, Maria, Emma, Network_User_2, Subscriber_2, Follower_A, Leo, Contact_Y, Alice, Kate, Victor, Community_Member_B, Isabel, Profile_A, we want to recommend potential friends. The existing friendships are: Contact_X and User_Beta are connected. Contact_X is friends ...
["Contact_X", "Maria", "Subscriber_2", "Leo", "Profile_A"]
(0, 16)
[[0, 1], [0, 4], [0, 5], [0, 11], [1, 3], [1, 4], [1, 6], [1, 14], [2, 3], [2, 8], [2, 15], [3, 4], [3, 8], [3, 10], [3, 11], [4, 6], [4, 8], [4, 15], [5, 8], [5, 10], [5, 12], [6, 11], [6, 12], [7, 11], [7, 12], [7, 13], [8, 9], [8, 11], [8, 12], [8, 14], [9, 12], [9, 13], [11, 12], [11, 13], [11, 14], [11, 15], [12, ...
undirected
[0, 0]
{"Contact_X": "0", "User_Beta": "1", "Zack": "2", "Maria": "3", "Emma": "4", "Network_User_2": "5", "Subscriber_2": "6", "Follower_A": "7", "Leo": "8", "Contact_Y": "9", "Alice": "10", "Kate": "11", "Victor": "12", "Community_Member_B": "13", "Isabel": "14", "Profile_A": "15"}
none
node_list
real_erdos
common_neighbor
friend_recommendation
Given a friendship network with members Nathan, Profile_B, Member_2, Friend_3, Victor, Charlie, Uma, Subscriber_1, Contact_Y, Frank and relationships Nathan has Subscriber_1 in their social circle. Nathan has Contact_Y in their social circle. There is a friendship between Profile_B and Charlie. There is a friendship be...
["Subscriber_1"]
(0, 10)
[[0, 7], [0, 8], [1, 5], [1, 9], [2, 3], [2, 7], [3, 6], [4, 5], [4, 6], [4, 8], [6, 7], [7, 9]]
undirected
[0, 0]
{"Nathan": "0", "Profile_B": "1", "Member_2": "2", "Friend_3": "3", "Victor": "4", "Charlie": "5", "Uma": "6", "Subscriber_1": "7", "Contact_Y": "8", "Frank": "9"}
none
node_list
real_erdos
common_neighbor
friend_recommendation
Given a friendship network with members Uma, Member_1, Emma, Grace, Henry, Friend_2, Frank, Network_User_1, Network_User_2, Diana, Wendy, Profile_B, Contact_Y, Charlie, Nathan, Contact_X, Community_Member_A, Alice, User_Gamma, Subscriber_1, Xavier, Victor and relationships Uma has Grace in their social circle. Uma is f...
["Emma", "Grace", "Network_User_2"]
(0, 22)
[[0, 3], [0, 4], [0, 8], [0, 14], [0, 15], [0, 16], [0, 17], [0, 19], [0, 20], [0, 21], [1, 2], [1, 7], [1, 9], [1, 10], [1, 14], [1, 15], [1, 16], [1, 17], [1, 19], [2, 5], [2, 7], [2, 10], [2, 12], [2, 13], [2, 14], [2, 17], [2, 21], [3, 4], [3, 9], [3, 10], [3, 11], [3, 15], [3, 16], [3, 20], [3, 21], [4, 8], [4, 9]...
undirected
[0, 0]
{"Uma": "0", "Member_1": "1", "Emma": "2", "Grace": "3", "Henry": "4", "Friend_2": "5", "Frank": "6", "Network_User_1": "7", "Network_User_2": "8", "Diana": "9", "Wendy": "10", "Profile_B": "11", "Contact_Y": "12", "Charlie": "13", "Nathan": "14", "Contact_X": "15", "Community_Member_A": "16", "Alice": "17", "User_Gamm...
none
node_list
real_erdos
common_neighbor
friend_recommendation
Given a friendship network with members Steve, Nathan, Charlie, Jack, Member_1, Profile_A, Follower_A, Profile_C, Yara, Maria, Leo, Follower_B, Frank, Uma, Friend_2, Victor, Contact_Y, Quinn, Olivia, Subscriber_1, Member_2 and relationships Steve is friends with Nathan. There is a friendship between Steve and Charlie. ...
["Steve"]
(0, 21)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 10], [0, 11], [0, 12], [0, 13], [0, 15], [0, 16], [0, 17], [0, 18], [0, 19], [0, 20], [2, 3], [2, 5], [2, 6], [2, 9], [2, 14], [3, 4], [3, 8], [3, 16], [3, 20], [4, 7], [5, 11], [5, 12], [5, 15], [6, 8], [8, 9], [8, 10], [8, 13], [8, 14], [8, 17], [12, 18], [...
undirected
[0, 0]
{"Steve": "0", "Nathan": "1", "Charlie": "2", "Jack": "3", "Member_1": "4", "Profile_A": "5", "Follower_A": "6", "Profile_C": "7", "Yara": "8", "Maria": "9", "Leo": "10", "Follower_B": "11", "Frank": "12", "Uma": "13", "Friend_2": "14", "Victor": "15", "Contact_Y": "16", "Quinn": "17", "Olivia": "18", "Subscriber_1": "...
none
node_list
real_erdos
common_neighbor
friend_recommendation
To improve friend suggestions, we're examining the network: Steve, Rachel, Zack, Alice, User_Gamma, Kate, Contact_Y, Yara, Friend_3, Network_User_2, User_Beta, Leo, Network_User_1. Friendship links: Steve is friends with Rachel. Steve follows Zack. Steve has Kate in their social circle. Steve and User_Beta are connecte...
["User_Beta"]
(0, 13)
[[0, 1], [0, 2], [0, 5], [0, 10], [0, 11], [1, 3], [1, 5], [2, 3], [2, 6], [2, 7], [2, 11], [2, 12], [3, 4], [3, 10], [3, 12], [4, 10], [4, 11], [5, 10], [5, 11], [6, 9], [6, 11], [7, 8], [8, 9], [9, 10], [10, 12]]
undirected
[0, 0]
{"Steve": "0", "Rachel": "1", "Zack": "2", "Alice": "3", "User_Gamma": "4", "Kate": "5", "Contact_Y": "6", "Yara": "7", "Friend_3": "8", "Network_User_2": "9", "User_Beta": "10", "Leo": "11", "Network_User_1": "12"}
none
node_list
real_erdos
common_neighbor
friend_recommendation
To improve friend suggestions, we're examining the network: Quinn, Contact_Z, Steve, Henry, Subscriber_2, Follower_A, Kate, User_Gamma, Charlie, Jack, Friend_2, User_Delta, Network_User_1, Member_2, Alice. Friendship links: Quinn has Contact_Z in their social circle. Quinn follows Steve. Quinn follows Henry. There is a...
["Contact_Z", "Henry", "Member_2"]
(0, 15)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 6], [0, 8], [0, 13], [0, 14], [1, 3], [1, 7], [1, 11], [3, 4], [3, 5], [3, 6], [3, 10], [3, 11], [4, 5], [4, 14], [5, 7], [6, 8], [6, 9], [6, 10], [8, 9], [8, 12], [9, 12], [11, 13]]
undirected
[0, 0]
{"Quinn": "0", "Contact_Z": "1", "Steve": "2", "Henry": "3", "Subscriber_2": "4", "Follower_A": "5", "Kate": "6", "User_Gamma": "7", "Charlie": "8", "Jack": "9", "Friend_2": "10", "User_Delta": "11", "Network_User_1": "12", "Member_2": "13", "Alice": "14"}
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Active_User_A, Follower_Y, Forum_Member_1, Audience_Member_1, Follower_X, Engaged_Member_2, Group_Participant_B, Hobbyist_A, User_B, Listener_B, Regular_Visitor_B, Community_User_B, Group_Participant_A, Network_Participant_2, Contributor_Alpha, Active_User_B, Listener_A...
["Community_User_B"]
(0, 24)
[[0, 2], [0, 7], [0, 8], [0, 10], [0, 20], [0, 21], [1, 23], [2, 13], [3, 14], [3, 17], [4, 8], [4, 10], [4, 14], [4, 16], [4, 19], [4, 20], [4, 21], [5, 8], [5, 13], [5, 14], [5, 18], [6, 11], [6, 18], [6, 21], [7, 11], [7, 13], [7, 15], [8, 10], [8, 11], [8, 13], [8, 16], [8, 19], [8, 20], [8, 21], [9, 11], [9, 19], ...
undirected
[0, 0]
{"Active_User_A": "0", "Follower_Y": "1", "Forum_Member_1": "2", "Audience_Member_1": "3", "Follower_X": "4", "Engaged_Member_2": "5", "Group_Participant_B": "6", "Hobbyist_A": "7", "User_B": "8", "Listener_B": "9", "Regular_Visitor_B": "10", "Community_User_B": "11", "Group_Participant_A": "12", "Network_Participant_2...
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Network_Participant_1, Active_User_B, Fan_1, Hobbyist_B, Member_X, Member_Y, Fan_2, Follower_X, Contributor_Beta, Participant_2, Subscriber_2, Subscriber_1, Engaged_Member_1, Group_Participant_B, Active_User_A, Viewer_B, Enthusiast_1, Group_Participant_A, Community_User...
["Active_User_B", "Fan_1", "Community_User_A"]
(0, 23)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 11], [0, 12], [0, 16], [0, 18], [0, 20], [0, 21], [1, 10], [1, 12], [2, 3], [2, 8], [2, 10], [2, 15], [2, 16], [3, 4], [3, 5], [3, 6], [3, 7], [3, 9], [3, 11], [3, 13], [3, 14], [4, 7], [4, 14], [4, 22], [5, 9], [5, 15], [5, 17], [5, 19], [7, 8], [7, 17], [8, 21], [9...
undirected
[0, 0]
{"Network_Participant_1": "0", "Active_User_B": "1", "Fan_1": "2", "Hobbyist_B": "3", "Member_X": "4", "Member_Y": "5", "Fan_2": "6", "Follower_X": "7", "Contributor_Beta": "8", "Participant_2": "9", "Subscriber_2": "10", "Subscriber_1": "11", "Engaged_Member_1": "12", "Group_Participant_B": "13", "Active_User_A": "14"...
none
node_list
real_erdos
common_neighbor
interest_detection
Given a community graph of Society_Member_B, Club_Member_2, User_A, Club_Member_1, Reader_1, Hobbyist_A, Network_Participant_1, User_B, Contributor_Alpha, Subscriber_2, Community_User_B, Follower_Y, Forum_Member_1, Engaged_Member_1, Society_Member_A with interaction patterns There is activity overlap between Society_Me...
["Society_Member_B", "Society_Member_A"]
(0, 15)
[[0, 1], [0, 2], [0, 3], [0, 5], [0, 8], [0, 9], [0, 11], [0, 12], [1, 4], [1, 5], [1, 6], [1, 13], [2, 3], [2, 4], [2, 10], [2, 12], [3, 14], [4, 6], [4, 7], [4, 9], [6, 7], [6, 8], [6, 10], [6, 13], [7, 11], [11, 14]]
undirected
[0, 0]
{"Society_Member_B": "0", "Club_Member_2": "1", "User_A": "2", "Club_Member_1": "3", "Reader_1": "4", "Hobbyist_A": "5", "Network_Participant_1": "6", "User_B": "7", "Contributor_Alpha": "8", "Subscriber_2": "9", "Community_User_B": "10", "Follower_Y": "11", "Forum_Member_1": "12", "Engaged_Member_1": "13", "Society_Me...
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Reader_1, Network_Participant_2, Follower_X, Society_Member_A, Member_X, Reader_2, Network_Participant_1, Listener_A, User_A, Audience_Member_2, Audience_Member_1, Regular_Visitor_A, Member_Y, Consumer_A, Follower_Y, Fan_2, Active_User_A, Group_Participant_A, Club_Membe...
["Reader_1", "Reader_2", "Audience_Member_2", "Group_Participant_A"]
(0, 24)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 8], [0, 10], [0, 11], [0, 14], [0, 15], [0, 19], [0, 20], [0, 22], [0, 23], [1, 5], [1, 7], [1, 12], [1, 16], [1, 19], [1, 21], [3, 5], [3, 6], [3, 7], [3, 8], [3, 9], [3, 12], [3, 13], [3, 15], [3, 17], [3, 19], [3, 23], [4, 5], [4, 6], [4, 7], [4, 9], [4, 13], [4, ...
undirected
[0, 0]
{"Reader_1": "0", "Network_Participant_2": "1", "Follower_X": "2", "Society_Member_A": "3", "Member_X": "4", "Reader_2": "5", "Network_Participant_1": "6", "Listener_A": "7", "User_A": "8", "Audience_Member_2": "9", "Audience_Member_1": "10", "Regular_Visitor_A": "11", "Member_Y": "12", "Consumer_A": "13", "Follower_Y"...
none
node_list
real_erdos
common_neighbor
interest_detection
To match users by interests, examine the network: Follower_Y, Fan_2, Reader_1, Member_X, Subscriber_2, Listener_B, Regular_Visitor_B, Group_Participant_A, Contributor_Beta, Club_Member_2, User_A with engagement links: Follower_Y shares interests with Fan_2. Fan_2 and Listener_B engage with similar content. Fan_2 has co...
["Group_Participant_A"]
(0, 11)
[[0, 1], [1, 5], [1, 8], [1, 9], [3, 6], [4, 6], [4, 7], [6, 9], [7, 8], [7, 9], [7, 10], [8, 9]]
undirected
[0, 0]
{"Follower_Y": "0", "Fan_2": "1", "Reader_1": "2", "Member_X": "3", "Subscriber_2": "4", "Listener_B": "5", "Regular_Visitor_B": "6", "Group_Participant_A": "7", "Contributor_Beta": "8", "Club_Member_2": "9", "User_A": "10"}
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Community_User_B, Contributor_Alpha, Member_X, Reader_1, Audience_Member_1, Subscriber_1, Group_Participant_A, Participant_1, Consumer_A, Reader_2, Subscriber_2, Forum_Member_2, Member_Y, Club_Member_1, Engaged_Member_1, Viewer_A, Fan_1, Hobbyist_A, Follower_X, Active_U...
["Contributor_Alpha"]
(0, 25)
[[0, 1], [0, 2], [0, 6], [0, 9], [0, 10], [0, 20], [0, 24], [1, 3], [1, 4], [1, 5], [1, 7], [1, 8], [1, 9], [1, 10], [1, 11], [1, 15], [1, 18], [2, 3], [3, 4], [3, 5], [3, 6], [3, 7], [3, 8], [3, 12], [3, 13], [3, 14], [3, 16], [3, 17], [3, 19], [3, 22], [5, 15], [7, 14], [7, 19], [7, 23], [8, 12], [8, 21], [10, 11], [...
undirected
[0, 0]
{"Community_User_B": "0", "Contributor_Alpha": "1", "Member_X": "2", "Reader_1": "3", "Audience_Member_1": "4", "Subscriber_1": "5", "Group_Participant_A": "6", "Participant_1": "7", "Consumer_A": "8", "Reader_2": "9", "Subscriber_2": "10", "Forum_Member_2": "11", "Member_Y": "12", "Club_Member_1": "13", "Engaged_Membe...
none
node_list
real_erdos
common_neighbor
interest_detection
Given a community graph of Community_User_A, Community_User_B, Engaged_Member_1, Reader_2, Member_Y, Watcher_1, Subscriber_2, Regular_Visitor_B, Follower_Y, Listener_B, Reader_1, Network_Participant_2, Club_Member_1 with interaction patterns Community_User_A and Reader_2 participate in similar topics. Community_User_A ...
["Subscriber_2"]
(0, 13)
[[0, 3], [0, 4], [0, 7], [1, 4], [1, 5], [1, 7], [1, 11], [2, 6], [2, 7], [3, 11], [5, 8], [5, 10], [6, 8], [6, 9], [6, 10], [6, 12], [8, 10], [9, 12], [11, 12]]
undirected
[0, 0]
{"Community_User_A": "0", "Community_User_B": "1", "Engaged_Member_1": "2", "Reader_2": "3", "Member_Y": "4", "Watcher_1": "5", "Subscriber_2": "6", "Regular_Visitor_B": "7", "Follower_Y": "8", "Listener_B": "9", "Reader_1": "10", "Network_Participant_2": "11", "Club_Member_1": "12"}
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Contributor_Beta, Fan_2, Member_X, Member_Y, Active_User_A, Viewer_A, Active_User_B, Contributor_Alpha, Forum_Member_1, Listener_B, Hobbyist_B, Participant_2, Enthusiast_2, Network_Participant_1, Engaged_Member_2, Audience_Member_1. Interaction data: Contributor_Beta an...
["Contributor_Beta", "Forum_Member_1"]
(0, 16)
[[0, 5], [0, 7], [0, 8], [0, 9], [0, 11], [0, 13], [1, 10], [1, 12], [2, 3], [2, 6], [2, 8], [3, 5], [3, 8], [3, 11], [3, 14], [3, 15], [5, 13], [6, 9], [6, 11], [7, 10], [8, 11], [8, 13], [9, 13], [9, 15], [11, 12], [12, 15]]
undirected
[0, 0]
{"Contributor_Beta": "0", "Fan_2": "1", "Member_X": "2", "Member_Y": "3", "Active_User_A": "4", "Viewer_A": "5", "Active_User_B": "6", "Contributor_Alpha": "7", "Forum_Member_1": "8", "Listener_B": "9", "Hobbyist_B": "10", "Participant_2": "11", "Enthusiast_2": "12", "Network_Participant_1": "13", "Engaged_Member_2": "...
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Hobbyist_B, Audience_Member_1, Follower_Y, Active_User_A, Reader_1, Consumer_B, Club_Member_1, Subscriber_1, Listener_B, Contributor_Alpha, Viewer_A, Fan_2, Participant_2, Watcher_2, Network_Participant_2, User_A, Watcher_1, Contributor_Beta, Regular_Visitor_A. Interact...
["Follower_Y"]
(0, 19)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 6], [0, 12], [0, 17], [1, 3], [1, 5], [1, 9], [1, 10], [2, 5], [2, 6], [2, 7], [2, 8], [2, 11], [2, 15], [2, 16], [3, 4], [3, 7], [3, 10], [3, 11], [3, 13], [3, 18], [4, 9], [4, 16], [7, 8], [7, 15], [8, 12], [8, 13], [9, 17], [11, 14], [12, 14], [14, 18]]
undirected
[0, 0]
{"Hobbyist_B": "0", "Audience_Member_1": "1", "Follower_Y": "2", "Active_User_A": "3", "Reader_1": "4", "Consumer_B": "5", "Club_Member_1": "6", "Subscriber_1": "7", "Listener_B": "8", "Contributor_Alpha": "9", "Viewer_A": "10", "Fan_2": "11", "Participant_2": "12", "Watcher_2": "13", "Network_Participant_2": "14", "Us...
none
node_list
real_erdos
common_neighbor
interest_detection
Given a community graph of Watcher_1, Consumer_B, Group_Participant_A, Watcher_2, Reader_2, Active_User_A, Network_Participant_1, Consumer_A, Group_Participant_B, Listener_A, Hobbyist_B, Participant_1, Forum_Member_2 with interaction patterns There is activity overlap between Watcher_1 and Watcher_2. Watcher_1 shares i...
["Watcher_2", "Active_User_A"]
(0, 13)
[[0, 3], [0, 5], [1, 7], [1, 9], [1, 12], [2, 11], [2, 12], [3, 4], [3, 6], [3, 7], [3, 8], [3, 12], [4, 9], [4, 10], [4, 12], [5, 7], [5, 9], [6, 10], [7, 10]]
undirected
[0, 0]
{"Watcher_1": "0", "Consumer_B": "1", "Group_Participant_A": "2", "Watcher_2": "3", "Reader_2": "4", "Active_User_A": "5", "Network_Participant_1": "6", "Consumer_A": "7", "Group_Participant_B": "8", "Listener_A": "9", "Hobbyist_B": "10", "Participant_1": "11", "Forum_Member_2": "12"}
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Listener_B, User_B, User_A, Member_X, Follower_X, Enthusiast_1, Society_Member_B, Forum_Member_1, Fan_1, Hobbyist_B, Active_User_A, Subscriber_1, Watcher_2, Viewer_A, Participant_1, Forum_Member_2, Network_Participant_2, Society_Member_A, Contributor_Alpha, Engaged_Memb...
["Society_Member_B", "Fan_1", "Hobbyist_B"]
(0, 24)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [0, 10], [0, 14], [0, 16], [0, 18], [0, 19], [0, 20], [1, 5], [1, 6], [1, 8], [1, 13], [1, 21], [1, 22], [2, 6], [2, 8], [2, 11], [2, 13], [2, 16], [3, 5], [3, 7], [3, 8], [3, 9], [3, 10], [3, 11], [3, 15], [3, 21], [3, 23], [4, 5], [4, 6], [5, 7]...
undirected
[0, 0]
{"Listener_B": "0", "User_B": "1", "User_A": "2", "Member_X": "3", "Follower_X": "4", "Enthusiast_1": "5", "Society_Member_B": "6", "Forum_Member_1": "7", "Fan_1": "8", "Hobbyist_B": "9", "Active_User_A": "10", "Subscriber_1": "11", "Watcher_2": "12", "Viewer_A": "13", "Participant_1": "14", "Forum_Member_2": "15", "Ne...
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Member_X, Subscriber_2, Club_Member_1, Listener_A, Follower_Y, Fan_2, User_A, Watcher_1, Hobbyist_B, Contributor_Alpha, Member_Y, Audience_Member_2, Watcher_2, Society_Member_A, Participant_2. Interaction data: Member_X has common hobbies with User_A. Subscriber_2 has c...
["Watcher_2"]
(0, 15)
[[0, 6], [1, 14], [2, 3], [2, 13], [2, 14], [3, 6], [3, 11], [3, 12], [4, 5], [4, 7], [4, 10], [5, 14], [6, 9], [7, 9], [7, 14], [9, 10], [9, 11], [9, 12], [10, 12], [11, 14], [12, 13]]
undirected
[0, 0]
{"Member_X": "0", "Subscriber_2": "1", "Club_Member_1": "2", "Listener_A": "3", "Follower_Y": "4", "Fan_2": "5", "User_A": "6", "Watcher_1": "7", "Hobbyist_B": "8", "Contributor_Alpha": "9", "Member_Y": "10", "Audience_Member_2": "11", "Watcher_2": "12", "Society_Member_A": "13", "Participant_2": "14"}
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Follower_Y, Club_Member_2, Subscriber_2, Listener_B, Engaged_Member_2, Regular_Visitor_B, Follower_X, Society_Member_B, Group_Participant_B, Viewer_B, Community_User_B, Enthusiast_2, Member_X, Watcher_2, Reader_1, Member_Y, Community_User_A, Fan_1, Audience_Member_2, Au...
["Enthusiast_2", "Reader_1"]
(0, 23)
[[0, 3], [0, 6], [0, 7], [0, 11], [0, 15], [1, 2], [1, 3], [1, 7], [1, 21], [2, 10], [2, 11], [2, 13], [2, 14], [2, 15], [2, 17], [2, 20], [2, 22], [3, 12], [3, 13], [3, 17], [3, 22], [4, 5], [4, 8], [4, 11], [4, 22], [5, 7], [5, 8], [5, 12], [5, 22], [6, 7], [6, 8], [6, 17], [6, 19], [7, 20], [8, 10], [8, 14], [8, 18]...
undirected
[0, 0]
{"Follower_Y": "0", "Club_Member_2": "1", "Subscriber_2": "2", "Listener_B": "3", "Engaged_Member_2": "4", "Regular_Visitor_B": "5", "Follower_X": "6", "Society_Member_B": "7", "Group_Participant_B": "8", "Viewer_B": "9", "Community_User_B": "10", "Enthusiast_2": "11", "Member_X": "12", "Watcher_2": "13", "Reader_1": "...
none
node_list
real_erdos
common_neighbor
interest_detection
Given a community graph of Participant_1, Enthusiast_2, Community_User_B, Society_Member_A, Member_Y, Follower_Y, Subscriber_1, Reader_1, Hobbyist_A, Engaged_Member_1, Consumer_A, Engaged_Member_2, Group_Participant_A, Fan_1, Consumer_B, Fan_2, Audience_Member_1, Active_User_A, Regular_Visitor_B, Audience_Member_2, Wat...
["Fan_2", "Regular_Visitor_B"]
(0, 22)
[[1, 12], [1, 15], [1, 16], [1, 20], [2, 3], [2, 6], [2, 12], [2, 15], [2, 16], [2, 18], [3, 4], [3, 5], [3, 13], [3, 17], [3, 19], [4, 7], [4, 18], [4, 19], [5, 10], [5, 15], [5, 19], [6, 9], [6, 11], [6, 15], [6, 18], [6, 19], [7, 8], [7, 9], [7, 10], [7, 12], [7, 13], [7, 21], [8, 9], [8, 16], [8, 17], [9, 13], [9, ...
undirected
[0, 0]
{"Participant_1": "0", "Enthusiast_2": "1", "Community_User_B": "2", "Society_Member_A": "3", "Member_Y": "4", "Follower_Y": "5", "Subscriber_1": "6", "Reader_1": "7", "Hobbyist_A": "8", "Engaged_Member_1": "9", "Consumer_A": "10", "Engaged_Member_2": "11", "Group_Participant_A": "12", "Fan_1": "13", "Consumer_B": "14"...
none
node_list
real_erdos
common_neighbor
interest_detection
Given a community graph of Active_User_A, Enthusiast_1, Regular_Visitor_B, Follower_Y, Community_User_B, User_B, Viewer_A, Consumer_A, Engaged_Member_1, Regular_Visitor_A, Group_Participant_B, Participant_1, Forum_Member_1, Group_Participant_A, Fan_2, Society_Member_A with interaction patterns There is activity overlap...
["Consumer_A"]
(0, 16)
[[0, 6], [0, 9], [0, 10], [1, 4], [1, 6], [1, 15], [2, 6], [2, 7], [2, 8], [2, 9], [2, 10], [2, 15], [3, 5], [3, 6], [4, 14], [5, 10], [6, 9], [7, 8], [7, 9], [7, 11], [7, 12], [7, 13], [8, 9], [8, 10], [8, 14], [8, 15], [9, 11], [9, 15], [10, 12], [10, 15], [13, 14]]
undirected
[0, 0]
{"Active_User_A": "0", "Enthusiast_1": "1", "Regular_Visitor_B": "2", "Follower_Y": "3", "Community_User_B": "4", "User_B": "5", "Viewer_A": "6", "Consumer_A": "7", "Engaged_Member_1": "8", "Regular_Visitor_A": "9", "Group_Participant_B": "10", "Participant_1": "11", "Forum_Member_1": "12", "Group_Participant_A": "13",...
none
node_list
real_erdos
common_neighbor
interest_detection
In an online community with users Subscriber_1, Forum_Member_2, Reader_1, Hobbyist_A, Watcher_2, Participant_1, Participant_2, Society_Member_B, Group_Participant_A, Viewer_A, Consumer_A, Viewer_B, Consumer_B, Enthusiast_1, User_A, Member_X, Network_Participant_1, Engaged_Member_2, User_B, Hobbyist_B, Forum_Member_1, F...
["Subscriber_1", "Viewer_B"]
(0, 22)
[[0, 1], [0, 2], [0, 3], [0, 5], [0, 8], [0, 14], [0, 16], [0, 18], [1, 3], [1, 4], [1, 7], [1, 8], [1, 10], [1, 11], [1, 15], [1, 16], [1, 18], [2, 9], [2, 13], [2, 20], [3, 4], [3, 5], [3, 6], [3, 10], [3, 15], [4, 9], [4, 12], [4, 17], [4, 21], [5, 6], [5, 7], [5, 11], [5, 12], [5, 13], [5, 19], [6, 19], [8, 20], [1...
undirected
[0, 0]
{"Subscriber_1": "0", "Forum_Member_2": "1", "Reader_1": "2", "Hobbyist_A": "3", "Watcher_2": "4", "Participant_1": "5", "Participant_2": "6", "Society_Member_B": "7", "Group_Participant_A": "8", "Viewer_A": "9", "Consumer_A": "10", "Viewer_B": "11", "Consumer_B": "12", "Enthusiast_1": "13", "User_A": "14", "Member_X":...
none
node_list
real_erdos
common_neighbor
interest_detection
To match users by interests, examine the network: Listener_A, Community_User_A, Society_Member_B, Club_Member_1, Enthusiast_2, Forum_Member_1, Regular_Visitor_B, Viewer_B, Consumer_A, Fan_2, Hobbyist_A, Fan_1, Network_Participant_2, Active_User_A, Audience_Member_2, Contributor_Beta, Member_X, Watcher_2, Forum_Member_2...
["Community_User_A", "Viewer_B", "Contributor_Beta", "Member_X"]
(0, 23)
[[0, 1], [0, 4], [0, 5], [0, 9], [0, 10], [0, 17], [0, 21], [1, 5], [1, 7], [1, 8], [1, 9], [1, 11], [1, 13], [1, 16], [1, 18], [1, 19], [1, 21], [1, 22], [2, 7], [2, 8], [2, 9], [2, 11], [2, 21], [3, 8], [3, 9], [3, 13], [3, 15], [3, 16], [3, 17], [3, 22], [4, 13], [4, 19], [5, 20], [5, 22], [6, 7], [6, 10], [6, 11], ...
undirected
[0, 0]
{"Listener_A": "0", "Community_User_A": "1", "Society_Member_B": "2", "Club_Member_1": "3", "Enthusiast_2": "4", "Forum_Member_1": "5", "Regular_Visitor_B": "6", "Viewer_B": "7", "Consumer_A": "8", "Fan_2": "9", "Hobbyist_A": "10", "Fan_1": "11", "Network_Participant_2": "12", "Active_User_A": "13", "Audience_Member_2"...
none
node_list
real_erdos
common_neighbor
interest_detection
To match users by interests, examine the network: Contributor_Beta, Enthusiast_1, Community_User_A, Community_User_B, Consumer_A, Fan_2, Subscriber_1, Listener_B, Watcher_1, Hobbyist_A, Enthusiast_2, Participant_2, Engaged_Member_1, User_A, Contributor_Alpha, Society_Member_B, Member_X, Hobbyist_B, Watcher_2, Active_Us...
["Contributor_Beta"]
(0, 23)
[[0, 1], [0, 2], [0, 3], [0, 6], [0, 7], [0, 13], [0, 18], [0, 19], [1, 5], [1, 9], [1, 11], [2, 7], [2, 9], [2, 13], [2, 21], [2, 22], [3, 5], [3, 8], [3, 9], [3, 22], [4, 5], [4, 8], [4, 9], [4, 11], [4, 17], [4, 21], [5, 7], [5, 13], [5, 14], [5, 18], [6, 9], [6, 13], [6, 16], [6, 18], [6, 19], [6, 21], [7, 13], [7,...
undirected
[0, 0]
{"Contributor_Beta": "0", "Enthusiast_1": "1", "Community_User_A": "2", "Community_User_B": "3", "Consumer_A": "4", "Fan_2": "5", "Subscriber_1": "6", "Listener_B": "7", "Watcher_1": "8", "Hobbyist_A": "9", "Enthusiast_2": "10", "Participant_2": "11", "Engaged_Member_1": "12", "User_A": "13", "Contributor_Alpha": "14",...
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Watcher_2, Watcher_1, Listener_B, Viewer_A, Active_User_B, Follower_Y, User_A, Fan_2, Club_Member_2, Club_Member_1, Contributor_Alpha, Hobbyist_B, Society_Member_A, Contributor_Beta, Society_Member_B, Engaged_Member_2, Active_User_A, Forum_Member_1, User_B, Group_Partic...
["Watcher_2", "Club_Member_2"]
(0, 20)
[[0, 4], [0, 7], [0, 9], [0, 12], [0, 14], [0, 15], [1, 3], [1, 10], [1, 11], [1, 14], [1, 17], [1, 18], [1, 19], [2, 3], [2, 5], [2, 7], [2, 8], [2, 15], [3, 6], [3, 7], [3, 16], [3, 18], [4, 8], [5, 8], [5, 17], [6, 14], [6, 17], [7, 16], [7, 18], [8, 9], [8, 10], [8, 14], [8, 17], [9, 14], [9, 16], [9, 17], [10, 12]...
undirected
[0, 0]
{"Watcher_2": "0", "Watcher_1": "1", "Listener_B": "2", "Viewer_A": "3", "Active_User_B": "4", "Follower_Y": "5", "User_A": "6", "Fan_2": "7", "Club_Member_2": "8", "Club_Member_1": "9", "Contributor_Alpha": "10", "Hobbyist_B": "11", "Society_Member_A": "12", "Contributor_Beta": "13", "Society_Member_B": "14", "Engaged...
none
node_list
real_erdos
common_neighbor
interest_detection
A content platform tracks user engagement. Users: Club_Member_1, Subscriber_1, Subscriber_2, User_B, Follower_Y, Regular_Visitor_A, Listener_A, Watcher_1, Forum_Member_2, Network_Participant_2. Interaction data: There is activity overlap between Club_Member_1 and User_B. Club_Member_1 shares interests with Regular_Visi...
["User_B"]
(0, 10)
[[0, 3], [0, 5], [1, 4], [1, 5], [1, 6], [2, 3], [2, 7], [2, 9], [3, 8], [4, 5], [4, 7], [4, 8], [5, 9], [6, 8], [8, 9]]
undirected
[0, 0]
{"Club_Member_1": "0", "Subscriber_1": "1", "Subscriber_2": "2", "User_B": "3", "Follower_Y": "4", "Regular_Visitor_A": "5", "Listener_A": "6", "Watcher_1": "7", "Forum_Member_2": "8", "Network_Participant_2": "9"}
none
node_list
real_erdos
common_neighbor
link_prediction
In a knowledge graph with entities Resource_C, Term_A, Entry_1, Entity_A, Node_Alpha, Class_1, Content_1, Category_B, Element_1, Item_Y, Record_A, Element_2, Topic_C, Object_2, Data_Point_B, Topic_B, Object_1, we have relationships: There is a semantic connection between Resource_C and Term_A. Resource_C and Entry_1 ar...
["Element_1"]
(0, 17)
[[0, 1], [0, 2], [0, 3], [0, 5], [0, 6], [0, 11], [0, 12], [1, 4], [1, 8], [1, 10], [2, 3], [2, 4], [2, 7], [2, 15], [2, 16], [3, 5], [3, 7], [3, 14], [5, 6], [6, 8], [6, 11], [6, 13], [6, 16], [7, 9], [8, 9], [8, 10], [8, 12], [8, 13], [8, 14], [8, 15]]
undirected
[0, 0]
{"Resource_C": "0", "Term_A": "1", "Entry_1": "2", "Entity_A": "3", "Node_Alpha": "4", "Class_1": "5", "Content_1": "6", "Category_B": "7", "Element_1": "8", "Item_Y": "9", "Record_A": "10", "Element_2": "11", "Topic_C": "12", "Object_2": "13", "Data_Point_B": "14", "Topic_B": "15", "Object_1": "16"}
none
node_list
real_erdos
common_neighbor
link_prediction
In a knowledge graph with entities Document_B, Topic_C, Node_Gamma, Type_A, Item_X, Type_B, Node_Beta, Item_Y, Resource_A, Term_A, Subject_1, Resource_B, Keyword_1, Class_2, Content_2, Node_Alpha, Item_Z, Concept_2, we have relationships: Document_B is related to Topic_C. Document_B references Type_A. Document_B and Su...
["Node_Gamma", "Type_A", "Subject_1"]
(0, 18)
[[0, 1], [0, 3], [0, 10], [0, 12], [1, 4], [1, 15], [2, 6], [2, 14], [2, 15], [2, 17], [3, 7], [3, 10], [3, 12], [3, 14], [3, 17], [4, 5], [4, 6], [4, 16], [4, 17], [5, 12], [6, 8], [6, 12], [7, 9], [7, 11], [7, 12], [7, 14], [7, 15], [8, 11], [8, 16], [9, 15], [9, 16], [10, 11], [10, 14], [10, 16], [10, 17], [11, 13],...
undirected
[0, 0]
{"Document_B": "0", "Topic_C": "1", "Node_Gamma": "2", "Type_A": "3", "Item_X": "4", "Type_B": "5", "Node_Beta": "6", "Item_Y": "7", "Resource_A": "8", "Term_A": "9", "Subject_1": "10", "Resource_B": "11", "Keyword_1": "12", "Class_2": "13", "Content_2": "14", "Node_Alpha": "15", "Item_Z": "16", "Concept_2": "17"}
none
node_list
real_erdos
common_neighbor
link_prediction
To infer missing relationships in the graph: Node_Beta, Object_3, Resource_A, Node_Alpha, Content_2, Entity_A, Keyword_2, Record_A, Topic_C, Page_2, Element_1, Content_1, Article_A, Resource_C, Concept_1, Class_2, Data_Point_B, Item_Z, Document_B, Concept_3, Object_2, Resource_B, analyze existing links: There is a sema...
["Keyword_2"]
(0, 22)
[[0, 7], [0, 14], [0, 20], [0, 21], [1, 8], [1, 14], [1, 18], [2, 11], [2, 21], [3, 4], [3, 9], [4, 10], [5, 12], [5, 17], [6, 9], [6, 12], [7, 8], [7, 13], [7, 17], [8, 13], [8, 14], [8, 15], [8, 19], [9, 13], [10, 12], [10, 18], [10, 20], [11, 12], [11, 17], [11, 21], [12, 17], [13, 15], [13, 19], [14, 16], [14, 21],...
undirected
[0, 0]
{"Node_Beta": "0", "Object_3": "1", "Resource_A": "2", "Node_Alpha": "3", "Content_2": "4", "Entity_A": "5", "Keyword_2": "6", "Record_A": "7", "Topic_C": "8", "Page_2": "9", "Element_1": "10", "Content_1": "11", "Article_A": "12", "Resource_C": "13", "Concept_1": "14", "Class_2": "15", "Data_Point_B": "16", "Item_Z": ...
none
node_list
real_erdos
common_neighbor
link_prediction
Given a semantic network of concepts Concept_2, Record_B, Item_Y, Class_2, Instance_2, Category_B, Element_1, Type_A, Content_1, Entry_2, Object_3, Concept_3, Term_B, Object_2, Subject_2, Node_Gamma, Resource_A, Item_Z, Document_B, Page_2, Type_B, Concept_1, Keyword_1, Category_A, Article_B with associations Concept_2 ...
["Concept_2", "Element_1"]
(0, 25)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 9], [0, 10], [0, 11], [0, 13], [0, 15], [0, 17], [0, 20], [1, 4], [1, 5], [1, 6], [1, 9], [1, 10], [1, 12], [1, 18], [1, 20], [2, 4], [2, 5], [2, 6], [2, 8], [2, 10], [2, 18], [2, 20], [2, 21], [2, 23], [4, 7], [4, 24], [5, 16], [5, 19], [5, 21], [6, 7], [6, ...
undirected
[0, 0]
{"Concept_2": "0", "Record_B": "1", "Item_Y": "2", "Class_2": "3", "Instance_2": "4", "Category_B": "5", "Element_1": "6", "Type_A": "7", "Content_1": "8", "Entry_2": "9", "Object_3": "10", "Concept_3": "11", "Term_B": "12", "Object_2": "13", "Subject_2": "14", "Node_Gamma": "15", "Resource_A": "16", "Item_Z": "17", "D...
none
node_list
real_erdos
common_neighbor
link_prediction
In a knowledge graph with entities Resource_B, Term_A, Node_Beta, Article_A, Object_3, Instance_1, Document_A, Entity_C, Class_2, Object_1, Resource_C, Entry_2, Topic_C, Concept_3, Item_X, Keyword_1, Keyword_2, Element_2, Record_B, Item_Y, Article_B, Node_Gamma, Page_1, Category_B, we have relationships: There is a sem...
["Resource_B"]
(0, 24)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 8], [0, 9], [0, 14], [0, 15], [0, 18], [0, 20], [0, 21], [0, 22], [2, 3], [2, 4], [2, 10], [2, 12], [3, 5], [3, 9], [3, 10], [4, 7], [4, 13], [4, 19], [4, 23], [5, 6], [5, 7], [5, 8], [5, 11], [5, 12], [5, 13], [5, 17], [7, 14], [7, 16], [8, 17], [8, 23], [10, 11], [...
undirected
[0, 0]
{"Resource_B": "0", "Term_A": "1", "Node_Beta": "2", "Article_A": "3", "Object_3": "4", "Instance_1": "5", "Document_A": "6", "Entity_C": "7", "Class_2": "8", "Object_1": "9", "Resource_C": "10", "Entry_2": "11", "Topic_C": "12", "Concept_3": "13", "Item_X": "14", "Keyword_1": "15", "Keyword_2": "16", "Element_2": "17"...
none
node_list
real_erdos
common_neighbor
link_prediction
In a knowledge graph with entities Data_Point_B, Content_1, Entity_C, Term_B, Subject_1, Node_Gamma, Page_2, Element_2, Concept_1, Keyword_2, Node_Beta, we have relationships: Data_Point_B references Term_B. Data_Point_B has a relationship with Node_Beta. Content_1 and Node_Gamma are associated. Content_1 has a relatio...
["Element_2"]
(0, 11)
[[0, 3], [0, 10], [1, 5], [1, 7], [1, 9], [3, 4], [3, 6], [4, 9], [4, 10], [5, 6], [5, 10], [6, 7], [7, 9], [8, 9]]
undirected
[0, 0]
{"Data_Point_B": "0", "Content_1": "1", "Entity_C": "2", "Term_B": "3", "Subject_1": "4", "Node_Gamma": "5", "Page_2": "6", "Element_2": "7", "Concept_1": "8", "Keyword_2": "9", "Node_Beta": "10"}
none
node_list
real_erdos
common_neighbor
link_prediction
To infer missing relationships in the graph: Type_A, Document_B, Element_1, Topic_A, Page_1, Record_A, Category_A, Data_Point_A, Entry_1, Class_1, Resource_B, Term_B, Topic_B, Object_3, Content_1, Concept_3, Subject_1, Entity_A, Data_Point_B, Resource_A, Element_2, Term_A, Instance_1, Item_X, Keyword_1, analyze existin...
["Type_A", "Term_B", "Subject_1"]
(0, 25)
[[0, 3], [0, 17], [0, 20], [0, 23], [1, 4], [1, 8], [1, 17], [1, 19], [1, 21], [1, 22], [1, 23], [1, 24], [2, 4], [2, 11], [2, 12], [2, 13], [2, 16], [2, 21], [2, 22], [3, 6], [3, 7], [3, 11], [3, 13], [3, 16], [3, 20], [3, 21], [4, 6], [4, 16], [4, 17], [4, 20], [4, 21], [4, 23], [5, 6], [5, 9], [5, 12], [5, 18], [5, ...
undirected
[0, 0]
{"Type_A": "0", "Document_B": "1", "Element_1": "2", "Topic_A": "3", "Page_1": "4", "Record_A": "5", "Category_A": "6", "Data_Point_A": "7", "Entry_1": "8", "Class_1": "9", "Resource_B": "10", "Term_B": "11", "Topic_B": "12", "Object_3": "13", "Content_1": "14", "Concept_3": "15", "Subject_1": "16", "Entity_A": "17", "...
none
node_list
real_erdos
common_neighbor
link_prediction
In a knowledge graph with entities Topic_C, Item_Z, Object_1, Topic_B, Resource_C, Record_A, Item_X, Entity_A, Keyword_2, Page_1, Resource_B, Instance_2, Type_B, Object_2, Concept_1, Concept_2, Type_A, Topic_A, Keyword_1, Node_Alpha, Class_1, Page_2, Element_1, Node_Gamma, we have relationships: Topic_C is related to I...
["Topic_C", "Record_A"]
(0, 24)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [0, 13], [0, 14], [0, 18], [0, 19], [1, 5], [1, 6], [1, 7], [1, 8], [1, 10], [1, 11], [1, 12], [1, 15], [1, 23], [2, 5], [2, 7], [2, 10], [2, 15], [2, 16], [3, 5], [3, 6], [3, 8], [3, 9], [3, 10], [3, 12], [3, 16], [5, 6], [5, 9], [5, 10], [5, 11]...
undirected
[0, 0]
{"Topic_C": "0", "Item_Z": "1", "Object_1": "2", "Topic_B": "3", "Resource_C": "4", "Record_A": "5", "Item_X": "6", "Entity_A": "7", "Keyword_2": "8", "Page_1": "9", "Resource_B": "10", "Instance_2": "11", "Type_B": "12", "Object_2": "13", "Concept_1": "14", "Concept_2": "15", "Type_A": "16", "Topic_A": "17", "Keyword_...
none
node_list
real_erdos
common_neighbor
link_prediction
To infer missing relationships in the graph: Type_A, Element_1, Item_X, Resource_A, Subject_2, Class_2, Category_B, Concept_2, Subject_1, Element_2, Node_Beta, Node_Gamma, Category_A, Article_A, Topic_A, analyze existing links: Type_A and Element_1 are associated. Type_A and Item_X are associated. Type_A and Resource_A...
["Node_Beta"]
(0, 15)
[[0, 1], [0, 2], [0, 3], [0, 4], [1, 3], [1, 4], [1, 5], [1, 6], [1, 7], [1, 13], [2, 5], [2, 7], [2, 9], [2, 10], [2, 12], [3, 6], [3, 9], [3, 12], [3, 14], [5, 8], [5, 11], [6, 8], [6, 10], [9, 11], [10, 13], [10, 14]]
undirected
[0, 0]
{"Type_A": "0", "Element_1": "1", "Item_X": "2", "Resource_A": "3", "Subject_2": "4", "Class_2": "5", "Category_B": "6", "Concept_2": "7", "Subject_1": "8", "Element_2": "9", "Node_Beta": "10", "Node_Gamma": "11", "Category_A": "12", "Article_A": "13", "Topic_A": "14"}
none
node_list
real_erdos
common_neighbor
link_prediction
A knowledge base contains entities: Data_Point_A, Resource_B, Entity_B, Type_A, Element_2, Page_1, Type_B, Item_Z, Subject_1, Resource_A, Content_2, Element_1, Item_X, Subject_2 with relations: Data_Point_A references Item_Z. Data_Point_A references Subject_2. Entity_B is related to Type_B. Entity_B is related to Subje...
["Subject_1"]
(0, 14)
[[0, 7], [0, 13], [2, 6], [2, 8], [2, 9], [2, 12], [3, 4], [3, 7], [3, 13], [4, 11], [4, 12], [5, 6], [5, 11], [6, 9], [8, 10], [9, 13], [10, 11]]
undirected
[0, 0]
{"Data_Point_A": "0", "Resource_B": "1", "Entity_B": "2", "Type_A": "3", "Element_2": "4", "Page_1": "5", "Type_B": "6", "Item_Z": "7", "Subject_1": "8", "Resource_A": "9", "Content_2": "10", "Element_1": "11", "Item_X": "12", "Subject_2": "13"}
none
node_list
real_erdos
common_neighbor
link_prediction
In a knowledge graph with entities Entry_1, Keyword_1, Item_Y, Entity_B, Category_B, Concept_2, Concept_3, Class_1, Data_Point_A, Document_A, Instance_2, Page_1, Subject_1, Category_A, Concept_1, we have relationships: Entry_1 has a relationship with Keyword_1. There is a semantic connection between Entry_1 and Item_Y....
["Concept_3"]
(0, 15)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 9], [0, 11], [0, 12], [0, 13], [0, 14], [1, 8], [1, 10], [1, 11], [2, 3], [2, 8], [3, 4], [3, 5], [3, 6], [4, 12], [6, 7], [6, 10], [7, 9], [9, 13], [13, 14]]
undirected
[0, 0]
{"Entry_1": "0", "Keyword_1": "1", "Item_Y": "2", "Entity_B": "3", "Category_B": "4", "Concept_2": "5", "Concept_3": "6", "Class_1": "7", "Data_Point_A": "8", "Document_A": "9", "Instance_2": "10", "Page_1": "11", "Subject_1": "12", "Category_A": "13", "Concept_1": "14"}
none
node_list
real_erdos
common_neighbor
link_prediction
A knowledge base contains entities: Node_Alpha, Keyword_1, Entity_C, Document_B, Element_1, Type_B, Data_Point_B, Item_Z, Type_A, Class_2, Node_Beta, Subject_2, Resource_C, Entry_1, Node_Gamma, Class_1, Topic_C, Record_B, Concept_3, Page_2, Concept_2, Entity_B, Topic_B, Keyword_2 with relations: Node_Alpha is related t...
["Entry_1"]
(0, 24)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [0, 10], [0, 13], [0, 15], [0, 20], [0, 21], [0, 22], [0, 23], [1, 5], [1, 6], [1, 8], [1, 9], [1, 11], [1, 20], [1, 21], [2, 5], [2, 6], [2, 7], [2, 8], [2, 12], [2, 15], [2, 19], [3, 11], [3, 13], [3, 21], [4, 5], [4, 6], [4, 7], [4, 9], [5, 12]...
undirected
[0, 0]
{"Node_Alpha": "0", "Keyword_1": "1", "Entity_C": "2", "Document_B": "3", "Element_1": "4", "Type_B": "5", "Data_Point_B": "6", "Item_Z": "7", "Type_A": "8", "Class_2": "9", "Node_Beta": "10", "Subject_2": "11", "Resource_C": "12", "Entry_1": "13", "Node_Gamma": "14", "Class_1": "15", "Topic_C": "16", "Record_B": "17",...
none
node_list
real_erdos
common_neighbor
link_prediction
In a knowledge graph with entities Subject_1, Concept_1, Instance_2, Object_3, Page_1, Page_2, Entity_B, Topic_C, Data_Point_A, Element_2, Element_1, Content_2, Category_A, Term_A, Article_B, Document_B, Object_1, Concept_2, Node_Gamma, Entry_2, we have relationships: Subject_1 and Page_1 are associated. Subject_1 has ...
["Entity_B", "Document_B"]
(0, 20)
[[0, 4], [0, 12], [0, 14], [0, 16], [0, 17], [0, 18], [0, 19], [1, 5], [1, 8], [1, 17], [1, 19], [2, 3], [2, 6], [2, 9], [2, 12], [2, 15], [2, 19], [3, 4], [3, 7], [3, 8], [3, 16], [3, 18], [4, 10], [4, 13], [4, 14], [4, 15], [4, 18], [5, 6], [5, 8], [5, 9], [5, 12], [5, 15], [5, 19], [6, 7], [6, 8], [6, 9], [6, 12], [...
undirected
[0, 0]
{"Subject_1": "0", "Concept_1": "1", "Instance_2": "2", "Object_3": "3", "Page_1": "4", "Page_2": "5", "Entity_B": "6", "Topic_C": "7", "Data_Point_A": "8", "Element_2": "9", "Element_1": "10", "Content_2": "11", "Category_A": "12", "Term_A": "13", "Article_B": "14", "Document_B": "15", "Object_1": "16", "Concept_2": "...
none
node_list
real_erdos
common_neighbor
link_prediction
In a knowledge graph with entities Concept_2, Keyword_2, Subject_1, Entity_A, Article_A, Type_A, Item_Z, Category_B, Topic_C, Content_1, we have relationships: There is a semantic connection between Concept_2 and Keyword_2. Concept_2 references Subject_1. Concept_2 has a relationship with Content_1. Keyword_2 is relate...
["Type_A"]
(0, 10)
[[0, 1], [0, 2], [0, 9], [1, 2], [1, 5], [2, 3], [2, 5], [2, 9], [4, 7], [5, 6], [5, 7], [5, 8], [6, 7], [6, 9]]
undirected
[0, 0]
{"Concept_2": "0", "Keyword_2": "1", "Subject_1": "2", "Entity_A": "3", "Article_A": "4", "Type_A": "5", "Item_Z": "6", "Category_B": "7", "Topic_C": "8", "Content_1": "9"}
none
node_list
real_erdos
common_neighbor
link_prediction
To infer missing relationships in the graph: Class_1, Type_B, Instance_1, Data_Point_A, Entity_A, Object_3, Resource_A, Data_Point_B, Node_Beta, Resource_C, Record_A, Object_1, Topic_C, Instance_2, Record_B, Item_Z, Article_A, Item_Y, Element_2, analyze existing links: Class_1 references Type_B. Class_1 and Instance_1 ...
["Type_B", "Article_A"]
(0, 19)
[[0, 1], [0, 2], [0, 6], [0, 9], [0, 11], [0, 12], [0, 16], [0, 18], [1, 2], [1, 4], [1, 6], [1, 7], [1, 8], [1, 13], [1, 16], [2, 3], [2, 5], [2, 15], [2, 16], [3, 7], [3, 9], [3, 10], [3, 13], [3, 14], [3, 15], [4, 5], [4, 9], [4, 11], [4, 12], [4, 15], [5, 7], [5, 8], [5, 14], [5, 16], [5, 18], [6, 16], [6, 17], [7,...
undirected
[0, 0]
{"Class_1": "0", "Type_B": "1", "Instance_1": "2", "Data_Point_A": "3", "Entity_A": "4", "Object_3": "5", "Resource_A": "6", "Data_Point_B": "7", "Node_Beta": "8", "Resource_C": "9", "Record_A": "10", "Object_1": "11", "Topic_C": "12", "Instance_2": "13", "Record_B": "14", "Item_Z": "15", "Article_A": "16", "Item_Y": "...
none
node_list
real_erdos
common_neighbor
link_prediction
To infer missing relationships in the graph: Term_B, Concept_3, Instance_1, Entity_B, Record_A, Type_A, Concept_1, Item_X, Category_A, Type_B, Data_Point_A, Data_Point_B, Object_1, Page_2, Content_2, Item_Z, Class_1, analyze existing links: Term_B references Record_A. Term_B is related to Type_A. Term_B and Data_Point_...
["Page_2"]
(0, 17)
[[0, 4], [0, 5], [0, 10], [0, 12], [0, 13], [1, 9], [1, 10], [1, 11], [1, 13], [1, 15], [2, 3], [2, 4], [2, 6], [2, 12], [3, 8], [3, 11], [3, 12], [3, 13], [4, 7], [4, 8], [4, 12], [4, 15], [5, 6], [5, 13], [5, 16], [6, 13], [7, 9], [7, 11], [7, 14], [8, 9], [8, 16], [9, 14], [10, 14], [10, 16], [11, 12], [12, 15], [13...
undirected
[0, 0]
{"Term_B": "0", "Concept_3": "1", "Instance_1": "2", "Entity_B": "3", "Record_A": "4", "Type_A": "5", "Concept_1": "6", "Item_X": "7", "Category_A": "8", "Type_B": "9", "Data_Point_A": "10", "Data_Point_B": "11", "Object_1": "12", "Page_2": "13", "Content_2": "14", "Item_Z": "15", "Class_1": "16"}
none
node_list
real_erdos
common_neighbor
link_prediction
Given a semantic network of concepts Type_A, Resource_B, Topic_B, Class_2, Concept_1, Data_Point_B, Topic_A, Entry_2, Entity_A, Resource_A with associations Type_A and Topic_B are associated. There is a semantic connection between Type_A and Data_Point_B. There is a semantic connection between Type_A and Resource_A. Re...
["Topic_B"]
(0, 10)
[[0, 2], [0, 5], [0, 9], [1, 2], [1, 3], [2, 4], [2, 6], [2, 8], [3, 4], [3, 6], [4, 5], [4, 6], [4, 8], [5, 6], [5, 7], [7, 8]]
undirected
[0, 0]
{"Type_A": "0", "Resource_B": "1", "Topic_B": "2", "Class_2": "3", "Concept_1": "4", "Data_Point_B": "5", "Topic_A": "6", "Entry_2": "7", "Entity_A": "8", "Resource_A": "9"}
none
node_list
real_erdos
common_neighbor
link_prediction
Given a semantic network of concepts Article_A, Item_Z, Topic_A, Instance_2, Subject_2, Page_1, Entity_A, Resource_B, Term_B, Type_B, Entry_1, Class_2, Resource_A, Concept_2, Term_A, Content_1, Topic_B, Type_A, Concept_3, Item_X, Data_Point_B, Entity_B, Entry_2, Object_2 with associations Article_A has a relationship w...
["Resource_A"]
(0, 24)
[[0, 1], [0, 2], [0, 4], [0, 5], [0, 6], [0, 8], [0, 9], [0, 10], [0, 13], [0, 14], [0, 16], [0, 18], [0, 19], [0, 20], [1, 3], [1, 4], [1, 6], [1, 7], [1, 8], [1, 11], [1, 12], [1, 15], [2, 3], [3, 7], [4, 5], [6, 10], [6, 23], [7, 13], [8, 9], [8, 11], [8, 12], [8, 14], [8, 21], [10, 16], [10, 19], [10, 21], [11, 15]...
undirected
[0, 0]
{"Article_A": "0", "Item_Z": "1", "Topic_A": "2", "Instance_2": "3", "Subject_2": "4", "Page_1": "5", "Entity_A": "6", "Resource_B": "7", "Term_B": "8", "Type_B": "9", "Entry_1": "10", "Class_2": "11", "Resource_A": "12", "Concept_2": "13", "Term_A": "14", "Content_1": "15", "Topic_B": "16", "Type_A": "17", "Concept_3"...
none
node_list
real_erdos
common_neighbor
link_prediction
In a knowledge graph with entities Term_B, Topic_A, Resource_C, Entry_1, Node_Beta, Type_B, Item_Z, Entity_A, Data_Point_B, Object_2, Instance_2, Term_A, Page_1, Document_B, Topic_C, Node_Alpha, Element_1, Subject_2, Category_B, Node_Gamma, we have relationships: Term_B is related to Topic_A. There is a semantic connec...
["Type_B"]
(0, 20)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [0, 10], [0, 12], [0, 13], [0, 14], [0, 15], [0, 18], [1, 16], [2, 4], [2, 5], [2, 7], [2, 8], [2, 15], [2, 18], [3, 4], [3, 6], [3, 10], [3, 12], [4, 5], [4, 9], [4, 19], [5, 6], [5, 11], [5, 12], [5, 13], [5, 14], [5, 17], [5, 19], [6, 7], [6, 8...
undirected
[0, 0]
{"Term_B": "0", "Topic_A": "1", "Resource_C": "2", "Entry_1": "3", "Node_Beta": "4", "Type_B": "5", "Item_Z": "6", "Entity_A": "7", "Data_Point_B": "8", "Object_2": "9", "Instance_2": "10", "Term_A": "11", "Page_1": "12", "Document_B": "13", "Topic_C": "14", "Node_Alpha": "15", "Element_1": "16", "Subject_2": "17", "Ca...
none
node_list
real_erdos
common_neighbor
link_prediction
In a knowledge graph with entities Record_A, Resource_A, Resource_C, Element_2, Term_A, Object_1, Concept_3, Subject_2, Node_Alpha, Page_1, Object_2, Resource_B, Keyword_2, we have relationships: There is a semantic connection between Record_A and Keyword_2. Resource_A and Object_1 are associated. Resource_C is related...
["Node_Alpha"]
(0, 13)
[[0, 12], [1, 5], [2, 7], [2, 8], [2, 12], [4, 5], [4, 10], [4, 11], [5, 6], [5, 7], [5, 8], [5, 11], [6, 9], [7, 10], [8, 12], [9, 10], [9, 11]]
undirected
[0, 0]
{"Record_A": "0", "Resource_A": "1", "Resource_C": "2", "Element_2": "3", "Term_A": "4", "Object_1": "5", "Concept_3": "6", "Subject_2": "7", "Node_Alpha": "8", "Page_1": "9", "Object_2": "10", "Resource_B": "11", "Keyword_2": "12"}
none
node_list
real_erdos
common_neighbor
supplier_network
For supply chain optimization, analyze the network: Enterprise_Y, Corporation_2, Supplier_Chain_A, Distributor_Beta, Supplier_A, Warehouse_Operator_1, Distributor_Alpha, Transport_Company_B, Parts_Distributor_2, Supplier_Chain_B, Service_Provider_A, Enterprise_X, Component_Vendor_1, Supplier_B, Wholesaler_1, Firm_1, Ma...
["Component_Vendor_1", "Wholesaler_1"]
(0, 19)
[[0, 1], [0, 2], [0, 7], [0, 10], [0, 13], [0, 15], [0, 17], [0, 18], [1, 2], [1, 14], [1, 15], [1, 18], [2, 6], [2, 7], [2, 10], [2, 13], [2, 15], [2, 17], [3, 4], [3, 14], [3, 16], [3, 17], [4, 9], [4, 12], [4, 14], [5, 9], [5, 11], [5, 13], [6, 8], [6, 18], [7, 8], [7, 9], [8, 10], [8, 14], [8, 16], [8, 17], [9, 10]...
undirected
[0, 0]
{"Enterprise_Y": "0", "Corporation_2": "1", "Supplier_Chain_A": "2", "Distributor_Beta": "3", "Supplier_A": "4", "Warehouse_Operator_1": "5", "Distributor_Alpha": "6", "Transport_Company_B": "7", "Parts_Distributor_2": "8", "Supplier_Chain_B": "9", "Service_Provider_A": "10", "Enterprise_X": "11", "Component_Vendor_1":...
none
node_list
real_erdos
common_neighbor
supplier_network
Given a vendor network including Business_B, Supplier_Chain_A, Warehouse_Operator_1, Provider_A, Provider_B, Parts_Distributor_1, Service_Provider_B, Wholesaler_2, Fulfillment_Center_1, Company_B, Warehouse_Operator_2, Raw_Material_Source_B, Supplier_C, Transport_Company_B, Contractor_1, Material_Supplier_A, Company_A,...
["Company_A"]
(0, 24)
[[0, 6], [0, 10], [0, 12], [0, 13], [0, 19], [0, 22], [1, 8], [1, 13], [1, 16], [1, 23], [2, 5], [2, 7], [2, 8], [2, 9], [2, 10], [2, 16], [2, 18], [3, 4], [3, 9], [3, 12], [3, 14], [3, 17], [3, 18], [4, 7], [4, 9], [4, 10], [4, 14], [4, 20], [4, 21], [5, 9], [5, 10], [5, 12], [5, 14], [5, 17], [5, 22], [5, 23], [6, 14...
undirected
[0, 0]
{"Business_B": "0", "Supplier_Chain_A": "1", "Warehouse_Operator_1": "2", "Provider_A": "3", "Provider_B": "4", "Parts_Distributor_1": "5", "Service_Provider_B": "6", "Wholesaler_2": "7", "Fulfillment_Center_1": "8", "Company_B": "9", "Warehouse_Operator_2": "10", "Raw_Material_Source_B": "11", "Supplier_C": "12", "Tra...
none
node_list
real_erdos
common_neighbor
supplier_network
In a B2B network with participants Raw_Material_Source_B, Parts_Distributor_1, Firm_1, Business_B, Distributor_Alpha, Component_Vendor_1, Supplier_C, Fulfillment_Center_1, Manufacturer_Y, Enterprise_X, Supplier_Chain_B, Partner_Alpha, Vendor_1, Firm_2, Vendor_3, Transport_Company_B, Enterprise_Y and partnerships Raw_Ma...
["Raw_Material_Source_B"]
(0, 17)
[[0, 1], [0, 2], [0, 3], [0, 6], [0, 7], [0, 12], [1, 3], [1, 4], [1, 5], [1, 7], [1, 9], [1, 10], [1, 11], [1, 12], [1, 14], [1, 15], [2, 6], [2, 14], [2, 15], [2, 16], [3, 4], [3, 5], [3, 8], [3, 10], [5, 11], [6, 8], [8, 9], [9, 16], [10, 13], [11, 13]]
undirected
[0, 0]
{"Raw_Material_Source_B": "0", "Parts_Distributor_1": "1", "Firm_1": "2", "Business_B": "3", "Distributor_Alpha": "4", "Component_Vendor_1": "5", "Supplier_C": "6", "Fulfillment_Center_1": "7", "Manufacturer_Y": "8", "Enterprise_X": "9", "Supplier_Chain_B": "10", "Partner_Alpha": "11", "Vendor_1": "12", "Firm_2": "13",...
none
node_list
real_erdos
common_neighbor
supplier_network
For supply chain optimization, analyze the network: Provider_B, Wholesaler_1, Warehouse_Operator_1, Company_A, Partner_Alpha, Transport_Company_B, Manufacturer_Y, Contractor_1, Logistics_Provider_1, Supplier_Chain_B, Provider_A, Logistics_Provider_2, Vendor_2, Supplier_C, Firm_2, Company_B with connections: Provider_B ...
["Firm_2"]
(0, 16)
[[0, 2], [0, 6], [0, 7], [0, 14], [1, 2], [1, 5], [1, 6], [1, 7], [1, 10], [1, 14], [1, 15], [2, 11], [2, 12], [3, 4], [3, 14], [3, 15], [4, 8], [4, 12], [5, 8], [5, 13], [5, 14], [6, 8], [6, 14], [7, 8], [7, 10], [7, 12], [7, 13], [8, 9], [8, 14], [9, 10], [9, 11], [9, 14], [10, 12], [10, 14], [10, 15], [11, 13], [11,...
undirected
[0, 0]
{"Provider_B": "0", "Wholesaler_1": "1", "Warehouse_Operator_1": "2", "Company_A": "3", "Partner_Alpha": "4", "Transport_Company_B": "5", "Manufacturer_Y": "6", "Contractor_1": "7", "Logistics_Provider_1": "8", "Supplier_Chain_B": "9", "Provider_A": "10", "Logistics_Provider_2": "11", "Vendor_2": "12", "Supplier_C": "1...
none
node_list
real_erdos
common_neighbor
supplier_network
For supply chain optimization, analyze the network: Company_A, Firm_1, Raw_Material_Source_A, Contractor_2, Supplier_Chain_A, Enterprise_X, Material_Supplier_A, Partner_Beta, Supplier_Chain_B, Corporation_2, Transport_Company_B, Warehouse_Operator_2, Business_A, Material_Supplier_B, Manufacturer_Y, Warehouse_Operator_1...
["Corporation_2"]
(0, 21)
[[0, 1], [0, 5], [0, 7], [0, 9], [0, 12], [0, 15], [0, 17], [1, 3], [1, 5], [1, 7], [1, 9], [1, 10], [1, 14], [1, 16], [1, 17], [1, 18], [1, 19], [2, 7], [2, 8], [2, 13], [2, 18], [3, 5], [3, 9], [3, 13], [4, 6], [4, 8], [4, 9], [4, 11], [4, 12], [4, 16], [4, 19], [4, 20], [5, 8], [5, 14], [5, 15], [6, 11], [6, 17], [7...
undirected
[0, 0]
{"Company_A": "0", "Firm_1": "1", "Raw_Material_Source_A": "2", "Contractor_2": "3", "Supplier_Chain_A": "4", "Enterprise_X": "5", "Material_Supplier_A": "6", "Partner_Beta": "7", "Supplier_Chain_B": "8", "Corporation_2": "9", "Transport_Company_B": "10", "Warehouse_Operator_2": "11", "Business_A": "12", "Material_Supp...
none
node_list
real_erdos
common_neighbor
supplier_network
A supply chain network consists of companies: Enterprise_X, Contractor_1, Fulfillment_Center_1, Corporation_1, Supplier_A, Business_A, Raw_Material_Source_B, Component_Vendor_1, Manufacturer_Y, Parts_Distributor_2, Partner_Alpha, Supplier_Chain_B, Transport_Company_B, Supplier_Chain_A, Partner_Beta, Vendor_3, Material_...
["Business_A"]
(0, 21)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 10], [0, 11], [0, 14], [0, 15], [0, 18], [0, 20], [1, 4], [1, 5], [1, 8], [1, 12], [2, 3], [2, 13], [3, 6], [3, 7], [3, 8], [3, 16], [4, 5], [4, 7], [4, 9], [5, 6], [5, 12], [5, 13], [5, 16], [5, 19], [6, 20], [8, 9], [8, 18], [9, 10], [9, 11], [9, 17], [13, 14], [13, 15], [13, 17],...
undirected
[0, 0]
{"Enterprise_X": "0", "Contractor_1": "1", "Fulfillment_Center_1": "2", "Corporation_1": "3", "Supplier_A": "4", "Business_A": "5", "Raw_Material_Source_B": "6", "Component_Vendor_1": "7", "Manufacturer_Y": "8", "Parts_Distributor_2": "9", "Partner_Alpha": "10", "Supplier_Chain_B": "11", "Transport_Company_B": "12", "S...
none
node_list
real_erdos
common_neighbor
supplier_network
Given a vendor network including Warehouse_Operator_2, Vendor_2, Material_Supplier_B, Logistics_Provider_2, Corporation_2, Company_B, Parts_Distributor_1, Transport_Company_A, Corporation_1, Vendor_3, Provider_A, Material_Supplier_A, Vendor_1, Supplier_B, Partner_Alpha, Distributor_Alpha and business links Warehouse_Op...
["Material_Supplier_B"]
(0, 16)
[[0, 1], [0, 2], [0, 3], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [1, 4], [1, 8], [2, 3], [2, 4], [2, 5], [2, 6], [2, 11], [2, 13], [2, 14], [3, 9], [3, 14], [4, 7], [5, 10], [5, 13], [8, 10], [8, 11], [9, 12], [10, 12], [11, 15], [13, 15]]
undirected
[0, 0]
{"Warehouse_Operator_2": "0", "Vendor_2": "1", "Material_Supplier_B": "2", "Logistics_Provider_2": "3", "Corporation_2": "4", "Company_B": "5", "Parts_Distributor_1": "6", "Transport_Company_A": "7", "Corporation_1": "8", "Vendor_3": "9", "Provider_A": "10", "Material_Supplier_A": "11", "Vendor_1": "12", "Supplier_B": ...
none
node_list
real_erdos
common_neighbor
supplier_network
In a B2B network with participants Distributor_Alpha, Enterprise_Y, Vendor_2, Vendor_3, Warehouse_Operator_1, Firm_1, Logistics_Provider_2, Component_Vendor_1, Service_Provider_A, Fulfillment_Center_1, Provider_B, Supplier_Chain_B, Transport_Company_A, Corporation_2, Parts_Distributor_2, Company_A, Wholesaler_1, Enterp...
["Provider_B"]
(0, 24)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 13], [0, 15], [1, 4], [1, 6], [1, 10], [1, 11], [1, 12], [2, 4], [2, 7], [2, 8], [2, 12], [2, 16], [2, 20], [2, 22], [3, 5], [3, 6], [3, 9], [3, 17], [3, 21], [3, 22], [4, 5], [4, 9], [4, 10], [4, 17], [5, 7], [5, 8], [5, 9], [5, 11], [5, 14], [5, 16], [5, 17...
undirected
[0, 0]
{"Distributor_Alpha": "0", "Enterprise_Y": "1", "Vendor_2": "2", "Vendor_3": "3", "Warehouse_Operator_1": "4", "Firm_1": "5", "Logistics_Provider_2": "6", "Component_Vendor_1": "7", "Service_Provider_A": "8", "Fulfillment_Center_1": "9", "Provider_B": "10", "Supplier_Chain_B": "11", "Transport_Company_A": "12", "Corpor...
none
node_list
real_erdos
common_neighbor
supplier_network
A supply chain network consists of companies: Raw_Material_Source_B, Contractor_1, Corporation_2, Business_A, Warehouse_Operator_2, Service_Provider_A, Supplier_A, Enterprise_Y, Distributor_Beta, Warehouse_Operator_1, Transport_Company_A, Component_Vendor_2, Wholesaler_1, Partner_Beta, Firm_2, Supplier_Chain_B, Enterpr...
["Company_A"]
(0, 21)
[[0, 5], [0, 6], [0, 8], [0, 10], [0, 12], [0, 13], [1, 3], [1, 6], [1, 12], [1, 17], [1, 18], [2, 3], [2, 6], [2, 10], [2, 13], [2, 15], [3, 14], [3, 17], [4, 13], [4, 16], [5, 7], [5, 9], [5, 11], [5, 13], [5, 14], [5, 17], [5, 18], [5, 20], [6, 8], [6, 13], [6, 14], [6, 17], [6, 20], [7, 8], [7, 12], [7, 13], [7, 18...
undirected
[0, 0]
{"Raw_Material_Source_B": "0", "Contractor_1": "1", "Corporation_2": "2", "Business_A": "3", "Warehouse_Operator_2": "4", "Service_Provider_A": "5", "Supplier_A": "6", "Enterprise_Y": "7", "Distributor_Beta": "8", "Warehouse_Operator_1": "9", "Transport_Company_A": "10", "Component_Vendor_2": "11", "Wholesaler_1": "12"...
none
node_list
real_erdos
common_neighbor
supplier_network
For supply chain optimization, analyze the network: Corporation_2, Transport_Company_B, Firm_1, Provider_B, Wholesaler_1, Service_Provider_B, Fulfillment_Center_1, Raw_Material_Source_B, Partner_Alpha, Logistics_Provider_1, Material_Supplier_A, Fulfillment_Center_2, Supplier_Chain_A, Vendor_1, Contractor_1, Supplier_A,...
["Provider_B"]
(0, 19)
[[0, 1], [0, 2], [0, 3], [0, 5], [0, 6], [0, 10], [0, 13], [0, 15], [1, 3], [1, 4], [1, 8], [1, 17], [3, 4], [3, 8], [3, 12], [3, 13], [3, 14], [4, 5], [4, 7], [4, 9], [4, 14], [4, 18], [5, 6], [5, 7], [5, 9], [5, 11], [8, 10], [10, 11], [10, 12], [12, 16], [14, 15], [14, 16], [14, 18], [15, 17]]
undirected
[0, 0]
{"Corporation_2": "0", "Transport_Company_B": "1", "Firm_1": "2", "Provider_B": "3", "Wholesaler_1": "4", "Service_Provider_B": "5", "Fulfillment_Center_1": "6", "Raw_Material_Source_B": "7", "Partner_Alpha": "8", "Logistics_Provider_1": "9", "Material_Supplier_A": "10", "Fulfillment_Center_2": "11", "Supplier_Chain_A"...
none
node_list
real_erdos
common_neighbor
supplier_network
In a B2B network with participants Company_B, Contractor_2, Supplier_A, Supplier_Chain_A, Warehouse_Operator_2, Component_Vendor_2, Transport_Company_A, Raw_Material_Source_B, Firm_2, Vendor_3, Wholesaler_1, Manufacturer_Y, Manufacturer_X, Material_Supplier_A, Enterprise_X, Wholesaler_2, Raw_Material_Source_A, Business...
["Supplier_Chain_A"]
(0, 18)
[[0, 1], [0, 2], [0, 3], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [0, 10], [0, 11], [0, 12], [1, 3], [1, 4], [1, 5], [1, 9], [1, 13], [1, 14], [1, 15], [1, 16], [3, 4], [3, 7], [3, 8], [3, 17], [4, 10], [4, 14], [5, 6], [5, 13], [7, 11], [7, 15], [10, 12], [10, 17], [11, 16]]
undirected
[0, 0]
{"Company_B": "0", "Contractor_2": "1", "Supplier_A": "2", "Supplier_Chain_A": "3", "Warehouse_Operator_2": "4", "Component_Vendor_2": "5", "Transport_Company_A": "6", "Raw_Material_Source_B": "7", "Firm_2": "8", "Vendor_3": "9", "Wholesaler_1": "10", "Manufacturer_Y": "11", "Manufacturer_X": "12", "Material_Supplier_A...
none
node_list
real_erdos
common_neighbor
supplier_network
In a B2B network with participants Transport_Company_A, Material_Supplier_B, Parts_Distributor_1, Parts_Distributor_2, Logistics_Provider_2, Vendor_2, Warehouse_Operator_1, Material_Supplier_A, Contractor_2, Enterprise_X, Raw_Material_Source_A, Partner_Beta, Supplier_C and partnerships Transport_Company_A and Material_...
["Transport_Company_A"]
(0, 13)
[[0, 1], [0, 2], [0, 3], [0, 5], [0, 7], [0, 12], [1, 3], [1, 4], [1, 5], [1, 9], [1, 10], [3, 4], [3, 6], [3, 7], [3, 8], [4, 8], [4, 9], [4, 10], [5, 6], [5, 11], [6, 12], [9, 11]]
undirected
[0, 0]
{"Transport_Company_A": "0", "Material_Supplier_B": "1", "Parts_Distributor_1": "2", "Parts_Distributor_2": "3", "Logistics_Provider_2": "4", "Vendor_2": "5", "Warehouse_Operator_1": "6", "Material_Supplier_A": "7", "Contractor_2": "8", "Enterprise_X": "9", "Raw_Material_Source_A": "10", "Partner_Beta": "11", "Supplier...
none
node_list
real_erdos
common_neighbor
supplier_network
Given a vendor network including Firm_2, Logistics_Provider_1, Contractor_2, Provider_A, Supplier_B, Business_B, Fulfillment_Center_1, Partner_Beta, Material_Supplier_A, Company_B, Vendor_2, Contractor_1, Distributor_Alpha, Supplier_C, Component_Vendor_2 and business links There is a supply chain link between Firm_2 an...
["Provider_A"]
(0, 15)
[[0, 1], [0, 2], [0, 3], [0, 5], [0, 12], [0, 14], [1, 3], [1, 6], [1, 7], [2, 4], [3, 4], [3, 5], [3, 7], [3, 8], [3, 9], [3, 11], [5, 6], [5, 8], [6, 9], [7, 10], [7, 13], [8, 11], [9, 10], [9, 13], [10, 12], [13, 14]]
undirected
[0, 0]
{"Firm_2": "0", "Logistics_Provider_1": "1", "Contractor_2": "2", "Provider_A": "3", "Supplier_B": "4", "Business_B": "5", "Fulfillment_Center_1": "6", "Partner_Beta": "7", "Material_Supplier_A": "8", "Company_B": "9", "Vendor_2": "10", "Contractor_1": "11", "Distributor_Alpha": "12", "Supplier_C": "13", "Component_Ven...
none
node_list
real_erdos
common_neighbor
supplier_network
A supply chain network consists of companies: Transport_Company_A, Firm_2, Provider_A, Business_A, Supplier_A, Logistics_Provider_2, Supplier_Chain_A, Fulfillment_Center_2, Distributor_Alpha, Vendor_3, Distributor_Beta, Company_A, Vendor_2, Warehouse_Operator_2. Business relationships: Transport_Company_A sources from ...
["Logistics_Provider_2", "Company_A"]
(0, 14)
[[0, 5], [0, 7], [0, 8], [0, 11], [0, 12], [1, 5], [1, 10], [1, 11], [1, 13], [2, 10], [2, 13], [3, 9], [3, 10], [3, 13], [4, 7], [4, 9], [6, 9], [7, 8], [8, 12], [9, 11], [9, 12], [9, 13]]
undirected
[0, 0]
{"Transport_Company_A": "0", "Firm_2": "1", "Provider_A": "2", "Business_A": "3", "Supplier_A": "4", "Logistics_Provider_2": "5", "Supplier_Chain_A": "6", "Fulfillment_Center_2": "7", "Distributor_Alpha": "8", "Vendor_3": "9", "Distributor_Beta": "10", "Company_A": "11", "Vendor_2": "12", "Warehouse_Operator_2": "13"}
none
node_list
real_erdos
common_neighbor
supplier_network
Given a vendor network including Logistics_Provider_1, Transport_Company_A, Business_B, Component_Vendor_1, Supplier_Chain_A, Wholesaler_1, Provider_B, Transport_Company_B, Enterprise_Y, Service_Provider_B, Manufacturer_Y, Supplier_B, Supplier_C, Vendor_3, Distributor_Beta and business links Logistics_Provider_1 source...
["Wholesaler_1"]
(0, 15)
[[0, 4], [0, 10], [0, 11], [2, 7], [2, 9], [2, 14], [3, 10], [4, 5], [4, 10], [5, 8], [5, 12], [5, 13], [6, 8], [6, 9], [6, 10], [7, 10], [7, 14], [10, 14], [12, 14]]
undirected
[0, 0]
{"Logistics_Provider_1": "0", "Transport_Company_A": "1", "Business_B": "2", "Component_Vendor_1": "3", "Supplier_Chain_A": "4", "Wholesaler_1": "5", "Provider_B": "6", "Transport_Company_B": "7", "Enterprise_Y": "8", "Service_Provider_B": "9", "Manufacturer_Y": "10", "Supplier_B": "11", "Supplier_C": "12", "Vendor_3":...
none
node_list
real_erdos
common_neighbor
supplier_network
In a B2B network with participants Distributor_Beta, Contractor_2, Service_Provider_B, Warehouse_Operator_1, Raw_Material_Source_B, Contractor_1, Parts_Distributor_1, Provider_B, Supplier_Chain_A, Logistics_Provider_2, Corporation_2, Partner_Alpha, Manufacturer_Y, Raw_Material_Source_A, Business_A, Partner_Beta, Logist...
["Raw_Material_Source_B"]
(0, 24)
[[0, 4], [0, 5], [0, 11], [0, 22], [1, 5], [1, 6], [1, 8], [1, 9], [1, 11], [1, 21], [1, 22], [2, 7], [2, 13], [2, 14], [2, 20], [3, 7], [3, 17], [3, 19], [3, 21], [4, 19], [4, 21], [5, 6], [5, 8], [5, 12], [5, 16], [6, 12], [6, 17], [6, 22], [7, 15], [7, 17], [8, 10], [8, 15], [9, 13], [9, 16], [9, 18], [10, 19], [11,...
undirected
[0, 0]
{"Distributor_Beta": "0", "Contractor_2": "1", "Service_Provider_B": "2", "Warehouse_Operator_1": "3", "Raw_Material_Source_B": "4", "Contractor_1": "5", "Parts_Distributor_1": "6", "Provider_B": "7", "Supplier_Chain_A": "8", "Logistics_Provider_2": "9", "Corporation_2": "10", "Partner_Alpha": "11", "Manufacturer_Y": "...
none
node_list
real_erdos
common_neighbor
supplier_network
For supply chain optimization, analyze the network: Supplier_B, Partner_Beta, Raw_Material_Source_A, Enterprise_X, Firm_2, Wholesaler_1, Warehouse_Operator_1, Distributor_Beta, Company_B, Enterprise_Y, Supplier_A, Logistics_Provider_1, Service_Provider_A, Manufacturer_Y, Provider_B with connections: Supplier_B and Raw_...
["Enterprise_Y"]
(0, 15)
[[0, 2], [0, 6], [0, 11], [0, 12], [1, 2], [1, 7], [1, 10], [1, 12], [1, 14], [2, 7], [2, 12], [3, 5], [3, 12], [4, 12], [5, 6], [5, 9], [5, 13], [6, 7], [6, 9], [6, 14], [7, 11], [7, 14], [8, 11], [11, 13]]
undirected
[0, 0]
{"Supplier_B": "0", "Partner_Beta": "1", "Raw_Material_Source_A": "2", "Enterprise_X": "3", "Firm_2": "4", "Wholesaler_1": "5", "Warehouse_Operator_1": "6", "Distributor_Beta": "7", "Company_B": "8", "Enterprise_Y": "9", "Supplier_A": "10", "Logistics_Provider_1": "11", "Service_Provider_A": "12", "Manufacturer_Y": "13...
none
node_list
real_erdos
common_neighbor
supplier_network
A supply chain network consists of companies: Company_A, Contractor_1, Supplier_C, Material_Supplier_B, Corporation_2, Partner_Alpha, Wholesaler_2, Contractor_2, Enterprise_Y, Supplier_Chain_A, Component_Vendor_1, Raw_Material_Source_B, Business_A, Fulfillment_Center_2, Vendor_2, Supplier_B. Business relationships: Com...
["Company_A"]
(0, 16)
[[0, 1], [0, 5], [0, 8], [0, 9], [0, 13], [1, 3], [1, 8], [1, 11], [1, 12], [1, 14], [2, 4], [2, 5], [2, 10], [2, 11], [2, 13], [3, 7], [3, 15], [4, 7], [4, 12], [5, 6], [6, 8], [6, 9], [6, 13], [7, 8], [7, 13], [7, 15], [8, 11], [8, 12], [8, 14], [10, 11], [10, 12]]
undirected
[0, 0]
{"Company_A": "0", "Contractor_1": "1", "Supplier_C": "2", "Material_Supplier_B": "3", "Corporation_2": "4", "Partner_Alpha": "5", "Wholesaler_2": "6", "Contractor_2": "7", "Enterprise_Y": "8", "Supplier_Chain_A": "9", "Component_Vendor_1": "10", "Raw_Material_Source_B": "11", "Business_A": "12", "Fulfillment_Center_2"...
none
node_list
real_erdos
common_neighbor
supplier_network
A supply chain network consists of companies: Material_Supplier_A, Logistics_Provider_1, Company_B, Vendor_3, Corporation_1, Transport_Company_A, Wholesaler_2, Fulfillment_Center_2, Logistics_Provider_2, Distributor_Beta, Supplier_C. Business relationships: Material_Supplier_A partners with Wholesaler_2. Material_Suppl...
["Wholesaler_2"]
(0, 11)
[[0, 6], [0, 7], [0, 10], [1, 8], [2, 3], [2, 6], [2, 9], [3, 7], [3, 9], [4, 6], [4, 8], [4, 9], [5, 9], [6, 7], [6, 8], [6, 10], [8, 9]]
undirected
[0, 0]
{"Material_Supplier_A": "0", "Logistics_Provider_1": "1", "Company_B": "2", "Vendor_3": "3", "Corporation_1": "4", "Transport_Company_A": "5", "Wholesaler_2": "6", "Fulfillment_Center_2": "7", "Logistics_Provider_2": "8", "Distributor_Beta": "9", "Supplier_C": "10"}
none
node_list
real_erdos
common_neighbor
supplier_network
Given a vendor network including Component_Vendor_1, Company_A, Supplier_C, Transport_Company_A, Wholesaler_1, Vendor_2, Vendor_3, Wholesaler_2, Distributor_Alpha, Partner_Alpha, Parts_Distributor_1, Parts_Distributor_2, Enterprise_X, Warehouse_Operator_1, Supplier_B, Component_Vendor_2, Supplier_Chain_B, Business_B, S...
["Component_Vendor_1", "Distributor_Alpha", "Partner_Alpha"]
(0, 22)
[[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8], [0, 10], [0, 12], [0, 13], [0, 14], [0, 15], [0, 16], [0, 20], [1, 5], [1, 11], [1, 17], [1, 18], [2, 4], [2, 13], [2, 18], [3, 4], [3, 8], [3, 9], [3, 12], [4, 5], [4, 6], [4, 7], [4, 8], [4, 9], [4, 12], [4, 16], [4, 21], [5, 6], [5, 7], [5, 9], [5, 17]...
undirected
[0, 0]
{"Component_Vendor_1": "0", "Company_A": "1", "Supplier_C": "2", "Transport_Company_A": "3", "Wholesaler_1": "4", "Vendor_2": "5", "Vendor_3": "6", "Wholesaler_2": "7", "Distributor_Alpha": "8", "Partner_Alpha": "9", "Parts_Distributor_1": "10", "Parts_Distributor_2": "11", "Enterprise_X": "12", "Warehouse_Operator_1":...
none
node_list