Datasets:
metadata
task_categories:
- graph-ml
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)}")