Network Analysis with NetworkX Cheat Sheet

Back to Data ScienceUpdated 2026-05-15

NetworkX is a Python library for creating, manipulating, and analyzing complex networks of nodes and edges. It supports multiple graph types (directed, undirected, weighted, multi-edge), provides implementations of over 100 graph algorithms, and integrates seamlessly with scientific Python tools like NumPy, SciPy, and Matplotlib. The library's key strength is its practical API design: graphs are Python objects, nodes can be any hashable type, and edge/node attributes are stored as dictionaries, making NetworkX both powerful for research and accessible for rapid prototyping. One critical insight: NetworkX prioritizes readability and ease of use over raw performance—for massive graphs exceeding millions of edges, consider graph-specific libraries like graph-tool or dedicated GPU-accelerated frameworks.

What This Cheat Sheet Covers

This topic spans 28 focused tables and 178 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Graph Types and CreationTable 2: Node and Edge AttributesTable 3: Basic Graph OperationsTable 4: Graph File I/OTable 5: Centrality MeasuresTable 6: Community DetectionTable 7: Shortest Path AlgorithmsTable 8: Connectivity and ComponentsTable 9: Graph GeneratorsTable 10: Clustering and Triadic ClosureTable 11: Visualization and LayoutTable 12: Bipartite GraphsTable 13: Graph Matching and IsomorphismTable 14: Graph TraversalTable 15: Degree Properties and DistributionTable 16: Assortativity and MixingTable 17: Graph Properties and MetricsTable 18: Subgraph OperationsTable 19: Tree Operations and Spanning TreesTable 20: Link PredictionTable 21: Graph Neural Network IntegrationTable 22: Cycles and Acyclic PropertiesTable 23: Cliques and Dense SubgraphsTable 24: Maximum Flow and Minimum CutTable 25: Graph Similarity and Isomorphism MetricsTable 26: Graph ColoringTable 27: Graph MatricesTable 28: Graph Operations (Union, Intersection, Product)

Table 1: Graph Types and Creation

Type	Example	Description
Graph (undirected)	`G = nx.Graph()` `G.add_edges_from([(1,2), (2,3)])`	Undirected graph where edges have no direction; allows self-loops but not parallel edges between same node pair.
DiGraph (directed)	`G = nx.DiGraph()` `G.add_edge(1, 2)`	Directed graph where edges have direction (u→v distinct from v→u); supports self-loops.
MultiGraph	`G = nx.MultiGraph()` `G.add_edge(1, 2, weight=1.5)` `G.add_edge(1, 2, weight=2.0)`	Undirected graph allowing multiple edges (parallel edges) between same node pair; each edge can hold distinct attributes.
MultiDiGraph	`G = nx.MultiDiGraph()` `G.add_edge('A', 'B', key=0)` `G.add_edge('A', 'B', key=1)`	Directed graph supporting multiple directed edges between same node pair; useful for modeling transport networks with multiple routes.
add_node	`G.add_node(5, color='red')`	Adds single node with optional key-value attributes.

Table 1: Graph Types and Creation

Type	Example	Description
Graph (undirected)	`G = nx.Graph()` `G.add_edges_from([(1,2), (2,3)])`	Undirected graph where edges have no direction; allows self-loops but not parallel edges between same node pair.
DiGraph (directed)	`G = nx.DiGraph()` `G.add_edge(1, 2)`	Directed graph where edges have direction (u→v distinct from v→u); supports self-loops.
MultiGraph	`G = nx.MultiGraph()` `G.add_edge(1, 2, weight=1.5)` `G.add_edge(1, 2, weight=2.0)`	Undirected graph allowing multiple edges (parallel edges) between same node pair; each edge can hold distinct attributes.
MultiDiGraph	`G = nx.MultiDiGraph()` `G.add_edge('A', 'B', key=0)` `G.add_edge('A', 'B', key=1)`	Directed graph supporting multiple directed edges between same node pair; useful for modeling transport networks with multiple routes.
add_node	`G.add_node(5, color='red')`	Adds single node with optional key-value attributes.