How to Build Knowledge Graph Generation Pipelines From Text With kg-gen, NetworkX Analytics, and Interactive Visualizations
print(“\n” + “=”*70 + “\n SECTION 6 — NetworkX analytics\n” + “=”*70) def kg_to_networkx(graph): G = nx.MultiDiGraph() for e in graph.entities: G.add_node(e) for s, p, o in graph.relations: G.add_edge(s, o, label=p) return G G = kg_to_networkx(g_big) print(f”Nodes: {G.number_of_nodes()} Edges: {G.number_of_edges()}”) H = nx.Graph(G) deg_cent = nx.degree_centrality(H) btw_cent = nx.betweenness_centrality(H) pr_cent = nx.pagerank(nx.DiGraph(G)) if G.number_of_edges() else…
