Manipulating Graphs with NetworkX

NetworkX is a powerful pure-Python library for creating, manipulating, and analyzing graphs. This guide covers essential tasks using NetworkX.

Inspecting Directed Graphs: In-Edges and Out-Edges

For directed graphs (nx.DiGraph), you may want to know which edges point into or out of a node.

Example:

import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add some edges
G.add_edges_from([(1, 2), (2, 1), (2, 3), (3, 1)])

node = 1

# In-edges: edges ending at this node
in_edges = G.in_edges(node)
print("In-edges:", list(in_edges))    # Output: [(2, 1), (3, 1)]

# Out-edges: edges starting from this node
out_edges = G.out_edges(node)
print("Out-edges:", list(out_edges))  # Output: [(1, 2)]

• G.in_edges(node) returns edges directed into node
• G.out_edges(node) returns edges directed out of node

Serializing and Deserializing Graphs

When working with graphs, you may want to save (serialize) them to a file and load (deserialize) them later. There are two common approaches in NetworkX:

A. Using JSON (Node-Link Data)

Why?

• Human-readable
• Cross-language interoperability

Serialize to JSON:

import networkx as nx
import json

G = nx.path_graph(4)
data = nx.node_link_data(G)

with open("graph.json", "w") as f:
    json.dump(data, f)

Deserialize from JSON:

with open("graph.json", "r") as f:
    data = json.load(f)

G = nx.node_link_graph(data)

Tip: JSON is a popular serialization format and can be easily used with web apps or other languages.

B. Using Pickle

Why?

• Fastest method in Python
• Carries all Python-specific information
• Warning: Not safe for untrusted data, and not interoperable with other languages.

Serialize using Pickle:

import networkx as nx
import pickle

G = nx.cycle_graph(5)

with open("graph.gpickle", "wb") as f:
    pickle.dump(G, f)

Deserialize using Pickle:

with open("graph.gpickle", "rb") as f:
    G = pickle.load(f)