Datasets:

CSE472-blanket-challenge
/

phase1-graphs

graph_id stringlengths 14 14	adjacency_matrix listlengths 10 20	num_nodes int64 10 20	num_edges int64 9 99	density float64 0.1 0.27	method stringclasses 1 value	created_at stringdate 2025-11-07 01:21:03 2025-11-07 01:21:03	seed int64 32.5M 2.13B
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Phase 1 Graphs

Graphs used in Phase 1

Data Card

Field	Type	Description
`graph_id`	string	Unique identifier for the graph
`adjacency_matrix`	List[List[int]]	NxN binary adjacency matrix where A[i,j]=1 means i→j
`num_nodes`	int	Number of nodes in the DAG
`num_edges`	int	Number of edges in the DAG
`density`	float	Graph density (edges / max_possible_edges)
`method`	string	Graph generation method (e.g., "PA", "ER")

Graph Generation Settings

Generation Methods:
- Preferential Attachment (PA)
Variable Densities: Graphs with different sparsity levels
DAG Structure: All graphs are valid Directed Acyclic Graphs

Quick Start

Load Graphs

import datasets

dataset = load_dataset("CSE472-blanket-challenge/phase1-graphs", split="train")

Convert to NetworkX

import networkx as nx
import numpy as np

# Create directed graph from adjacency matrix
adj_matrix = np.asarray(graph["adjacency_matrix"])
dag = nx.from_numpy_array(adj_matrix, create_using=nx.DiGraph)

print(f"Is DAG: {nx.is_directed_acyclic_graph(dag)}")

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