gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/networkx/references/io.md

NetworkX Input/Output

Reading Graphs from Files

Adjacency List Format

# Read adjacency list (simple text format)
G = nx.read_adjlist('graph.adjlist')

# With node type conversion
G = nx.read_adjlist('graph.adjlist', nodetype=int)

# For directed graphs
G = nx.read_adjlist('graph.adjlist', create_using=nx.DiGraph())

# Write adjacency list
nx.write_adjlist(G, 'graph.adjlist')

Example adjacency list format:

# node neighbors
0 1 2
1 0 3 4
2 0 3
3 1 2 4
4 1 3

Edge List Format

# Read edge list
G = nx.read_edgelist('graph.edgelist')

# With node types and edge data
G = nx.read_edgelist('graph.edgelist',
                     nodetype=int,
                     data=(('weight', float),))

# Read weighted edge list
G = nx.read_weighted_edgelist('weighted.edgelist')

# Write edge list
nx.write_edgelist(G, 'graph.edgelist')

# Write weighted edge list
nx.write_weighted_edgelist(G, 'weighted.edgelist')

Example edge list format:

# source target
0 1
1 2
2 3
3 0

Example weighted edge list:

# source target weight
0 1 0.5
1 2 1.0
2 3 0.75

GML (Graph Modelling Language)

# Read GML (preserves all attributes)
G = nx.read_gml('graph.gml')

# Write GML
nx.write_gml(G, 'graph.gml')

GraphML Format

# Read GraphML (XML-based format)
G = nx.read_graphml('graph.graphml')

# Write GraphML
nx.write_graphml(G, 'graph.graphml')

# With specific encoding
nx.write_graphml(G, 'graph.graphml', encoding='utf-8')

GEXF (Graph Exchange XML Format)

# Read GEXF
G = nx.read_gexf('graph.gexf')

# Write GEXF
nx.write_gexf(G, 'graph.gexf')

Pajek Format

# Read Pajek .net files
G = nx.read_pajek('graph.net')

# Write Pajek format
nx.write_pajek(G, 'graph.net')

LEDA Format

# Read LEDA format
G = nx.read_leda('graph.leda')

# Write LEDA format
nx.write_leda(G, 'graph.leda')

Working with Pandas

From Pandas DataFrame

import pandas as pd

# Create graph from edge list DataFrame
df = pd.DataFrame({
    'source': [1, 2, 3, 4],
    'target': [2, 3, 4, 1],
    'weight': [0.5, 1.0, 0.75, 0.25]
})

# Create graph
G = nx.from_pandas_edgelist(df,
                            source='source',
                            target='target',
                            edge_attr='weight')

# With multiple edge attributes
G = nx.from_pandas_edgelist(df,
                            source='source',
                            target='target',
                            edge_attr=['weight', 'color', 'type'])

# Create directed graph
G = nx.from_pandas_edgelist(df,
                            source='source',
                            target='target',
                            create_using=nx.DiGraph())

To Pandas DataFrame

# Convert graph to edge list DataFrame
df = nx.to_pandas_edgelist(G)

# With specific edge attributes
df = nx.to_pandas_edgelist(G, source='node1', target='node2')

Adjacency Matrix with Pandas

# Create DataFrame from adjacency matrix
df = nx.to_pandas_adjacency(G, dtype=int)

# Create graph from adjacency DataFrame
G = nx.from_pandas_adjacency(df)

# For directed graphs
G = nx.from_pandas_adjacency(df, create_using=nx.DiGraph())

NumPy and SciPy Integration

Adjacency Matrix

import numpy as np

# To NumPy adjacency matrix
A = nx.to_numpy_array(G, dtype=int)

# With specific node order
nodelist = [1, 2, 3, 4, 5]
A = nx.to_numpy_array(G, nodelist=nodelist)

# From NumPy array
G = nx.from_numpy_array(A)

# For directed graphs
G = nx.from_numpy_array(A, create_using=nx.DiGraph())

Sparse Matrix (SciPy)

from scipy import sparse

# To sparse matrix
A = nx.to_scipy_sparse_array(G)

# With specific format (csr, csc, coo, etc.)
A_csr = nx.to_scipy_sparse_array(G, format='csr')

# From sparse matrix
G = nx.from_scipy_sparse_array(A)

JSON Format

Node-Link Format

import json

# To node-link format (good for d3.js)
data = nx.node_link_data(G)
with open('graph.json', 'w') as f:
    json.dump(data, f)

# From node-link format
with open('graph.json', 'r') as f:
    data = json.load(f)
G = nx.node_link_graph(data)

Adjacency Data Format

# To adjacency format
data = nx.adjacency_data(G)
with open('graph.json', 'w') as f:
    json.dump(data, f)

# From adjacency format
with open('graph.json', 'r') as f:
    data = json.load(f)
G = nx.adjacency_graph(data)

Tree Data Format

# For tree graphs
data = nx.tree_data(G, root=0)
with open('tree.json', 'w') as f:
    json.dump(data, f)

# From tree format
with open('tree.json', 'r') as f:
    data = json.load(f)
G = nx.tree_graph(data)

Pickle Format

Binary Pickle

import pickle

# Write pickle (preserves all Python objects)
with open('graph.pkl', 'wb') as f:
    pickle.dump(G, f)

# Read pickle
with open('graph.pkl', 'rb') as f:
    G = pickle.load(f)

# NetworkX convenience functions
nx.write_gpickle(G, 'graph.gpickle')
G = nx.read_gpickle('graph.gpickle')

CSV Files

Custom CSV Reading

import csv

# Read edges from CSV
G = nx.Graph()
with open('edges.csv', 'r') as f:
    reader = csv.DictReader(f)
    for row in reader:
        G.add_edge(row['source'], row['target'], weight=float(row['weight']))

# Write edges to CSV
with open('edges.csv', 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerow(['source', 'target', 'weight'])
    for u, v, data in G.edges(data=True):
        writer.writerow([u, v, data.get('weight', 1.0)])

Database Integration

SQL Databases

import sqlite3
import pandas as pd

# Read from SQL database via pandas
conn = sqlite3.connect('network.db')
df = pd.read_sql_query("SELECT source, target, weight FROM edges", conn)
G = nx.from_pandas_edgelist(df, 'source', 'target', edge_attr='weight')
conn.close()

# Write to SQL database
df = nx.to_pandas_edgelist(G)
conn = sqlite3.connect('network.db')
df.to_sql('edges', conn, if_exists='replace', index=False)
conn.close()

Graph Formats for Visualization

DOT Format (Graphviz)

# Write DOT file for Graphviz
nx.drawing.nx_pydot.write_dot(G, 'graph.dot')

# Read DOT file
G = nx.drawing.nx_pydot.read_dot('graph.dot')

# Generate directly to image (requires Graphviz)
from networkx.drawing.nx_pydot import to_pydot
pydot_graph = to_pydot(G)
pydot_graph.write_png('graph.png')

Cytoscape Integration

Cytoscape JSON

# Export for Cytoscape
data = nx.cytoscape_data(G)
with open('cytoscape.json', 'w') as f:
    json.dump(data, f)

# Import from Cytoscape
with open('cytoscape.json', 'r') as f:
    data = json.load(f)
G = nx.cytoscape_graph(data)

Specialized Formats

Matrix Market Format

from scipy.io import mmread, mmwrite

# Read Matrix Market
A = mmread('graph.mtx')
G = nx.from_scipy_sparse_array(A)

# Write Matrix Market
A = nx.to_scipy_sparse_array(G)
mmwrite('graph.mtx', A)

Shapefile (for Geographic Networks)

# Requires pyshp library
# Read geographic network from shapefile
G = nx.read_shp('roads.shp')

# Write to shapefile
nx.write_shp(G, 'network')

Format Selection Guidelines

Choose Based on Requirements

Adjacency List - Simple, human-readable, no attributes

• Best for: Simple unweighted graphs, quick viewing

Edge List - Simple, supports weights, human-readable

• Best for: Weighted graphs, importing/exporting data

GML/GraphML - Full attribute preservation, XML-based

• Best for: Complete graph serialization with all metadata

JSON - Web-friendly, JavaScript integration

• Best for: Web applications, d3.js visualizations

Pickle - Fast, preserves Python objects, binary

• Best for: Python-only storage, complex attributes

Pandas - Data analysis integration, DataFrame operations

• Best for: Data processing pipelines, statistical analysis

NumPy/SciPy - Numerical computation, sparse matrices

• Best for: Matrix operations, scientific computing

DOT - Visualization, Graphviz integration

• Best for: Creating visual diagrams

Performance Considerations

Large Graphs

For large graphs, consider:

# Use compressed formats
import gzip
with gzip.open('graph.adjlist.gz', 'wt') as f:
    nx.write_adjlist(G, f)

with gzip.open('graph.adjlist.gz', 'rt') as f:
    G = nx.read_adjlist(f)

# Use binary formats (faster)
nx.write_gpickle(G, 'graph.gpickle')  # Faster than text formats

# Use sparse matrices for adjacency
A = nx.to_scipy_sparse_array(G, format='csr')  # Memory efficient

Incremental Loading

For very large graphs:

# Load graph incrementally from edge list
G = nx.Graph()
with open('huge_graph.edgelist') as f:
    for line in f:
        u, v = line.strip().split()
        G.add_edge(u, v)

        # Process in chunks
        if G.number_of_edges() % 100000 == 0:
            print(f"Loaded {G.number_of_edges()} edges")

Error Handling

Robust File Reading

try:
    G = nx.read_graphml('graph.graphml')
except nx.NetworkXError as e:
    print(f"Error reading GraphML: {e}")
except FileNotFoundError:
    print("File not found")
    G = nx.Graph()

# Check if file format is supported
if os.path.exists('graph.txt'):
    with open('graph.txt') as f:
        first_line = f.readline()
        # Detect format and read accordingly