networkx

name networkx description Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks. license 3-clause BSD license metadata {"version":"1.2","skill-author":"K-Dense Inc."} NetworkX Overview NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs. Use this skill when working with network or graph data structures, including social networks, biological networks, transportation systems, citation networks, knowledge graphs, or any system involving relationships between entities. This skill targets NetworkX 3.x (current stable: 3.6, which requires Python >= 3.11). Several pre-3.0 APIs ( nx.info , nx.write_gpickle , nx.read_shp ) and the 3.4-era nx.random_tree no longer exist — current replacements are used throughout this skill. When to Use This Skill Invoke this skill when tasks involve: Creating graphs : Building network structures from data, adding nodes and edges with attributes Graph analysis : Computing centrality measures, finding shortest paths, detecting communities, measuring clustering Graph algorithms : Running standard algorithms like Dijkstra's, PageRank, minimum spanning trees, maximum flow Network generation : Creating synthetic networks (random, scale-free, small-world models) for testing or simulation Graph I/O : Reading from or writing to various formats (edge lists, GraphML, JSON, CSV, adjacency matrices) Visualization : Drawing and customizing network visualizations with matplotlib or interactive libraries Network comparison : Checking isomorphism, computing graph metrics, analyzing structural properties Core Capabilities 1. Graph Creation and Manipulation NetworkX supports four main graph types: Graph : Undirected graphs with single edges DiGraph : Directed graphs with one-way connections MultiGraph : Undirected graphs allowing multiple edges between nodes MultiDiGraph : Directed graphs with multiple edges Create graphs by: import networkx as nx # Create empty graph G = nx.Graph() # Add nodes (can be any hashable type) G.add_node( 1 ) G.add_nodes_from([ 2 , 3 , 4 ]) G.add_node( "protein_A" , type = 'enzyme' , weight= 1.5 ) # Add edges G.add_edge( 1 , 2 ) G.add_edges_from([( 1 , 3 ), ( 2 , 4 )]) G.add_edge( 1 , 4 , weight= 0.8 , relation= 'interacts' ) Reference : See references/graph-basics.md for comprehensive guidance on creating, modifying, examining, and managing graph structures, including working with attributes and subgraphs. 2. Graph Algorithms NetworkX provides extensive algorithms for network analysis: Shortest Paths : # Find shortest path path = nx.shortest_path(G, source= 1 , target= 5 ) length = nx.shortest_path_length(G, source= 1 , target= 5 , weight= 'weight' ) Centrality Measures : # Degree centrality degree_cent = nx.degree_centrality(G) # Betweenness centrality betweenness = nx.betweenness_centrality(G) # PageRank pagerank = nx.pagerank(G) Community Detection : from networkx.algorithms import community # Detect communities communities = community.greedy_modularity_communities(G) Connectivity : # Check connectivity is_connected = nx.is_connected(G) # Find connected components components = list (nx.connected_components(G)) Reference : See references/algorithms.md for detailed documentation on all available algorithms including shortest paths, centrality measures, clustering, community detection, flows, matching, tree algorithms, and graph traversal. 3. Graph Generators Create synthetic networks for testing, simulation, or modeling: Classic Graphs : # Complete graph G = nx.complete_graph(n= 10 ) # Cycle graph G = nx.cycle_graph(n= 20 ) # Known graphs G = nx.karate_club_graph() G = nx.petersen_graph() Random Networks : # Erdős-Rényi random graph G = nx.erdos_renyi_graph(n= 100 , p= 0.1 , seed= 42 ) # Barabási-Albert scale-free network G = nx.barabasi_albert_graph(n= 100 , m= 3 , seed= 42 ) # Watts-Strogatz small-world network G = nx.watts_strogatz_graph(n= 100 , k= 6 , p= 0.1 , seed= 42 ) Structured Networks : # Grid graph G = nx.grid_2d_graph(m= 5 , n= 7 ) # Random tree (random_tree was removed in NetworkX 3.4) G = nx.random_labeled_tree( 100 , seed= 42 ) Reference : See references/generators.md for comprehensive coverage of all graph generators including classic, random, lattice, bipartite, and specialized network models with detailed parameters and use cases. 4. Reading and Writing Graphs NetworkX supports numerous file formats and data sources: File Formats : # Edge list G = nx.read_edgelist( 'graph.edgelist' ) nx.write_edgelist(G, 'graph.edgelist' ) # GraphML (preserves attributes) G = nx.read_graphml( 'graph.graphml' ) nx.write_graphml(G, 'graph.graphml' ) # GML G = nx.read_gml( 'graph.gml' ) nx.write_gml(G, 'graph.gml' ) # JSON (node-link format; edge list is stored under the "edges" key # since NetworkX 3.6 — older files may use "links", see references/io.md) data = nx.node_link_data(G) G = nx.node_link_graph(data) Pandas Integration : import pandas as pd # From DataFrame df = pd.DataFrame({ 'source' : [ 1 , 2 , 3 ], 'target' : [ 2 , 3 , 4 ], 'weight' : [ 0.5 , 1.0 , 0.75 ]}) G = nx.from_pandas_edgelist(df, 'source' , 'target' , edge_attr= 'weight' ) # To DataFrame df = nx.to_pandas_edgelist(G) Matrix Formats : import numpy as np # Adjacency matrix A = nx.to_numpy_array(G) G = nx.from_numpy_array(A) # Sparse matrix A = nx.to_scipy_sparse_array(G) G = nx.from_scipy_sparse_array(A) Reference : See references/io.md for complete documentation on all I/O formats including CSV, SQL databases, Cytoscape, DOT, and guidance on format selection for different use cases. 5. Visualization Create clear and informative network visualizations: Basic Visualization : import matplotlib.pyplot as plt # Simple draw nx.draw(G, with_labels= True ) plt.show() # With layout pos = nx.spring_layout(G, seed= 42 ) nx.draw(G, pos=pos, with_labels= True , node_color= 'lightblue' , node_size= 500 ) plt.show() Customization : # Color by degree node_colors = [G.degree(n) for n in G.nodes()] nx.draw(G, node_color=node_colors, cmap=plt.cm.viridis) # Size by centrality centrality = nx.betweenness_centrality(G) node_sizes = [ 3000 * centrality[n] for n in G.nodes()] nx.draw(G, node_size=node_sizes) # Edge weights edge_widths = [ 3 * G[u][v].get( 'weight' , 1 ) for u, v in G.edges()] nx.draw(G, width=edge_widths) Layout Algorithms : # Spring layout (force-directed) pos = nx.spring_layout(G, seed= 42 ) # Circular layout pos = nx.circular_layout(G) # Kamada-Kawai layout pos = nx.kamada_kawai_layout(G) # Spectral layout pos = nx.spectral_layout(G) Publication Quality : plt.figure(figsize=( 12 , 8 )) pos = nx.spring_layout(G, seed= 42 ) nx.draw(G, pos=pos, node_color= 'lightblue' , node_size= 500 , edge_color= 'gray' , with_labels= True , font_size= 10 ) plt.title( 'Network Visualization' , fontsize= 16 ) plt.axis( 'off' ) plt.tight_layout() plt.savefig( 'network.png' , dpi= 300 , bbox_inches= 'tight' ) plt.savefig( 'network.pdf' , bbox_inches= 'tight' ) # Vector format Reference : See references/visualization.md for extensive documentation on visualization techniques including layout algorithms, customization options, interactive visualizations with Plotly and PyVis, 3D networks, and publication-quality figure creation. Working with NetworkX Installation Ensure NetworkX is installed: # Check if installed import networkx as nx print (nx.__version__) # Install if needed (via bash) # uv pip install networkx # uv pip install networkx[default] # With optional dependencies Common Workflow Pattern Most NetworkX tasks follow this pattern: Create or Load Graph : # From scratch G = nx.Graph() G.add_edges_from([( 1 , 2 ), ( 2 , 3 ), ( 3 , 4 )]) # Or load from file/data G = nx.read_edgelist( 'data.txt' ) Examine Structure : print ( f"Nodes: {G.number_of_nodes()} " ) print ( f"Edges: {G.number_of_edges()} " ) print ( f"Density: {nx.density(G)} " ) print ( f"Connected: {nx.is_connected(G)} " ) Analyze : # Compute metrics degree_cent = nx.degree_centrality(G) avg_clustering = nx.average_clustering(G) # Find paths path = nx.shortest_path(G, source= 1 , target= 4 ) # Detect communities communities = community.greedy_modularity_communities(G) Visualize : pos = nx.spring_layout(G, seed= 42 ) nx.draw(G, pos=pos, with_labels= True ) plt.show() Export Results : # Save graph nx.write_graphml(G, 'analyzed_network.graphml' ) # Save metrics df = pd.DataFrame({ 'node' : list (degree_cent.keys()), 'centrality' : list (degree_cent.values()) }) df.to_csv( 'centrality_results.csv' , index= False ) Important Considerations Floating Point Precision : When graphs contain floating-point numbers, all results are inherently approximate due to precision limitations. This can affect algorithm outcomes, particularly in minimum/maximum computations. Memory and Performance : Each time a script runs, graph data must be loaded into memory. For large networks: Use appropriate data structures (sparse matrices for large sparse graphs) Consider loading only necessary subgraphs Use efficient file formats (pickle for Python objects, compressed formats) Leverage approximate algorithms for very large networks (e.g., k parameter in centrality calculations) For heavy workloads, NetworkX 3.x supports drop-in accelerated backends via the backend= keyword or nx.config.backend_priority — e.g. nx-cugraph (GPU), nx-parallel (multicore), graphblas-algorithms (sparse linear algebra). Install the backend package and pass backend="cugraph" (or similar) to supported functions; no algorithm code changes needed. Node and Edge Types : Nodes can be any hashable Python object (numbers, strings, tuples, custom objects) Use meaningful identifiers for clarity When removing nodes, all incident edges are automatically removed Random Seeds : Always set random seeds for reproducibility in random graph generation and force-directed layouts: G = nx.erdos_renyi_graph(n= 100 , p= 0.1 , seed= 42 ) pos = nx.spring_layout(G, seed= 42 ) Quick Reference Basic Operations # Create G = nx.Graph() G.add_edge( 1 , 2 ) # Query G.number_of_nodes() G.number_of_edges() G.degree( 1 ) list (G.neighbors( 1 )) # Check G.has_node( 1 ) G.has_edge( 1 , 2 ) nx.is_connected(G) # Modify G.remove_node( 1 ) G.remove_edge( 1 , 2 ) G.clear() Essential Algorithms # Paths nx.shortest_path(G, source, target) nx.all_pairs_shortest_path(G) # Centrality nx.degree_centrality(G) nx.betweenness_centrality(G) nx.closeness_centrality(G) nx.pagerank(G) # Clustering nx.clustering(G) nx.average_clustering(G) # Components nx.connected_components(G) nx.strongly_connected_components(G) # Directed # Community community.greedy_modularity_communities(G) File I/O Quick Reference # Read nx.read_edgelist( 'file.txt' ) nx.read_graphml( 'file.graphml' ) nx.read_gml( 'file.gml' ) # Write nx.write_edgelist(G, 'file.txt' ) nx.write_graphml(G, 'file.graphml' ) nx.write_gml(G, 'file.gml' ) # Pandas