Network Analysis with NetworkX Cheat Sheet

Updated 2026-05-15

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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, 105 flashcards. 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)

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Table 1: Graph Types and Creation

Everything in NetworkX starts with choosing the right graph class, and that choice locks in two decisions: whether edges have direction, and whether parallel edges between the same pair are allowed. Plain Graph and DiGraph cover most work; the Multi variants exist for when two nodes can be joined by several distinct edges, like multiple flights between two cities. The rest of the table is how you populate one—adding nodes and edges singly or in bulk, with add_edge conveniently creating any missing endpoints for you.

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.
add_nodes_from	`G.add_nodes_from([1, 2, 3])` `G.add_nodes_from([(4, {'wt': 0.5})])`	• Adds multiple nodes from iterable • attributes via tuple `(node, dict)` format
add_edge	`G.add_edge(1, 2, weight=4.5)`	• Adds edge between two nodes with optional attributes • creates nodes if they don't exist.
add_edges_from	`G.add_edges_from([(1,2), (2,3)])` `G.add_edges_from([(1, 2, {'w': 3})])`	• Bulk edge addition from edge list • third element in tuple specifies attributes
add_weighted_edges_from	`G.add_weighted_edges_from([(1, 2, 0.5), (2, 3, 1.2)])`	• Adds edges with weight attribute from 3-tuples `(u, v, weight)` • specialized for weighted networks

Table 2: Node and Edge Attributes

Nodes and edges in NetworkX aren't just labels—each can carry arbitrary data stored as a dictionary, which is what lets you attach weights, colors, capacities, or any domain attribute to the network. There are two styles here: the bulk set_/get_node_attributes functions for writing many at once, and direct dictionary access like G[u][v]['weight'] for reading or tweaking a single one. Knowing both saves a lot of boilerplate.

Operation	Example	Description
set_node_attributes	`nx.set_node_attributes(G, {1: 'A', 2: 'B'}, 'label')`	• Assigns attributes to multiple nodes from dict • third arg is attribute name

The rest of this section is part of the full cheat sheet

Table 3: Basic Graph Operations

The day-to-day inspection and editing toolkit: count nodes and edges, list a node's neighbors, check its degree, test whether a node or edge exists, and remove things you no longer need. For directed graphs, note that neighbors returns only successors and that degree splits into separate in- and out-degree—a distinction that matters the moment direction enters the picture.

Operation	Example	Description
number_of_nodes	`n = G.number_of_nodes()` `n = len(G)`	• Returns count of nodes • `len(G)` is shorthand

The rest of this section is part of the full cheat sheet

Table 4: Graph File I/O

Getting graphs in and out of files, and bridging to the numeric world. The text formats trade off simplicity against fidelity—edge lists are dead simple, while GraphML preserves typed node and edge attributes and is the safe choice when your graph carries rich data. The matrix conversions at the bottom are the gateway to NumPy and SciPy: turn a graph into an adjacency matrix for linear-algebra work, or build a graph straight from one.

Format	Example	Description
read_edgelist	`G = nx.read_edgelist('graph.txt')` `G = nx.read_edgelist('g.txt', nodetype=int)`	• Reads graph from edge list text file • default delimiter is whitespace, nodes are strings unless nodetype specified

The rest of this section is part of the full cheat sheet

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.
add_nodes_from	`G.add_nodes_from([1, 2, 3])` `G.add_nodes_from([(4, {'wt': 0.5})])`	• Adds multiple nodes from iterable • attributes via tuple `(node, dict)` format
add_edge	`G.add_edge(1, 2, weight=4.5)`	• Adds edge between two nodes with optional attributes • creates nodes if they don't exist.
add_edges_from	`G.add_edges_from([(1,2), (2,3)])` `G.add_edges_from([(1, 2, {'w': 3})])`	• Bulk edge addition from edge list • third element in tuple specifies attributes
add_weighted_edges_from	`G.add_weighted_edges_from([(1, 2, 0.5), (2, 3, 1.2)])`	• Adds edges with weight attribute from 3-tuples `(u, v, weight)` • specialized for weighted networks

Table 2: Node and Edge Attributes

Operation	Example	Description
set_node_attributes	`nx.set_node_attributes(G, {1: 'A', 2: 'B'}, 'label')`	• Assigns attributes to multiple nodes from dict • third arg is attribute name

Table 3: Basic Graph Operations

Operation	Example	Description
number_of_nodes	`n = G.number_of_nodes()` `n = len(G)`	• Returns count of nodes • `len(G)` is shorthand

Table 4: Graph File I/O

Format	Example	Description
read_edgelist	`G = nx.read_edgelist('graph.txt')` `G = nx.read_edgelist('g.txt', nodetype=int)`	• Reads graph from edge list text file • default delimiter is whitespace, nodes are strings unless nodetype specified