Networkx Graph Analysis Agent Skill

pip install networkx matplotlib scipy pandas numpy

import networkx as nx

# Create a graph and add edges with weights
G = nx.karate_club_graph()
print(f"Nodes: {G.number_of_nodes()}, Edges: {G.number_of_edges()}")
# Nodes: 34, Edges: 78

# Compute centrality and find most central node
bc = nx.betweenness_centrality(G)
top_node = max(bc, key=bc.get)
print(f"Most central node: {top_node}, betweenness: {bc[top_node]:.3f}")

# Detect communities
from networkx.algorithms import community
comms = community.greedy_modularity_communities(G)
print(f"Communities found: {len(comms)}")

import networkx as nx

# Undirected graph (most common)
G = nx.Graph()
G.add_node("protein_A", type="kinase", weight=1.5)
G.add_nodes_from(["protein_B", "protein_C"])
G.add_edge("protein_A", "protein_B", weight=0.9, interaction="phosphorylation")
G.add_edges_from([("protein_B", "protein_C"), ("protein_A", "protein_C")])
print(f"Nodes: {G.number_of_nodes()}, Edges: {G.number_of_edges()}")
# Nodes: 3, Edges: 3

# Directed graph (gene regulation, citations)
D = nx.DiGraph()
D.add_edges_from([("TF1", "geneA"), ("TF1", "geneB"), ("TF2", "geneA")])
print(f"TF1 out-degree: {D.out_degree('TF1')}")  # 2

# MultiGraph (multiple relationship types between same nodes)
M = nx.MultiGraph()
M.add_edge("A", "B", key="binding", affinity=0.8)
M.add_edge("A", "B", key="regulation", effect="inhibition")
print(f"Edges between A-B: {M.number_of_edges('A', 'B')}")  # 2

import networkx as nx
G = nx.karate_club_graph()

# Query structure
print(f"Degree of node 0: {G.degree(0)}")
print(f"Neighbors of node 0: {list(G.neighbors(0))[:5]}")
print(f"Has edge 0-1: {G.has_edge(0, 1)}")

# Set and get attributes
G.nodes[0]["role"] = "instructor"
nx.set_node_attributes(G, {0: "high", 33: "high"}, "importance")
G[0][1]["weight"] = 0.95

# Iterate with data
for u, v, data in G.edges(data=True):
    if "weight" in data:
        print(f"  Edge {u}-{v}: weight={data['weight']}")
        break

# Subgraphs (returns read-only view; use .copy() for mutable)
H = G.subgraph([0, 1, 2, 3, 4, 5]).copy()
print(f"Subgraph: {H.number_of_nodes()} nodes, {H.number_of_edges()} edges")

import networkx as nx
G = nx.karate_club_graph()

degree_c = nx.degree_centrality(G)
between_c = nx.betweenness_centrality(G, weight="weight")
# For large graphs, approximate: nx.betweenness_centrality(G, k=100)
close_c = nx.closeness_centrality(G)
eigen_c = nx.eigenvector_centrality(G, max_iter=1000)
pr = nx.pagerank(G, alpha=0.85)

# Compare top nodes across measures
for name, metric in [("Degree", degree_c), ("Betweenness", between_c),
                     ("Closeness", close_c), ("PageRank", pr)]:
    top = max(metric, key=metric.get)
    print(f"{name:12s}: top node={top}, score={metric[top]:.4f}")

import networkx as nx
G = nx.karate_club_graph()

# Shortest path
path = nx.shortest_path(G, source=0, target=33)
length = nx.shortest_path_length(G, source=0, target=33)
print(f"Shortest path 0->33: {path} (length {length})")
print(f"Average shortest path length: {nx.average_shortest_path_length(G):.3f}")

# Connected components
print(f"Connected: {nx.is_connected(G)}")
components = list(nx.connected_components(G))
print(f"Components: {len(components)}, largest: {len(max(components, key=len))}")

# For directed graphs: strong/weak connectivity
D = nx.DiGraph([(0,1),(1,2),(2,0),(3,4)])
print(f"Strongly connected: {list(nx.strongly_connected_components(D))}")

# Connectivity measures
print(f"Node connectivity: {nx.node_connectivity(G)}")
print(f"Edge connectivity: {nx.edge_connectivity(G)}")

import networkx as nx
from networkx.algorithms import community
import itertools

G = nx.karate_club_graph()

# Greedy modularity maximization
comms_greedy = community.greedy_modularity_communities(G)
mod_score = community.modularity(G, comms_greedy)
print(f"Greedy: {len(comms_greedy)} communities, modularity={mod_score:.4f}")

# Label propagation (fast, non-deterministic)
comms_lpa = community.label_propagation_communities(G)
print(f"Label propagation: {len(list(comms_lpa))} communities")

# Girvan-Newman (hierarchical, edge betweenness removal)
gn = community.girvan_newman(G)
# Get first level of partition
first_level = next(gn)
print(f"Girvan-Newman first split: {len(first_level)} groups")
print(f"  Sizes: {[len(c) for c in first_level]}")

import networkx as nx
import pandas as pd
import json

G = nx.karate_club_graph()

# Edge list (simple text format)
nx.write_edgelist(G, "karate.edgelist")
G_loaded = nx.read_edgelist("karate.edgelist", nodetype=int)

# GraphML (preserves all attributes, XML-based)
nx.write_graphml(G, "karate.graphml")
G_xml = nx.read_graphml("karate.graphml")

# JSON (node-link format, web-friendly for d3.js)
data = nx.node_link_data(G)
with open("karate.json", "w") as f:
    json.dump(data, f)

# Pandas integration
df = pd.DataFrame({"source": [1,2,3], "target": [2,3,4], "weight": [0.5,1.0,0.75]})
G_pd = nx.from_pandas_edgelist(df, "source", "target", edge_attr="weight")
df_out = nx.to_pandas_edgelist(G_pd)
print(f"Pandas round-trip: {len(df_out)} edges")

# NumPy/SciPy matrices
A = nx.to_numpy_array(G)
print(f"Adjacency matrix shape: {A.shape}")
A_sparse = nx.to_scipy_sparse_array(G, format="csr")  # Memory-efficient

import networkx as nx
import matplotlib.pyplot as plt

G = nx.karate_club_graph()
pos = nx.spring_layout(G, seed=42)

# Color by degree, size by betweenness centrality
bc = nx.betweenness_centrality(G)
fig, ax = plt.subplots(figsize=(10, 8))
nx.draw(G, pos=pos, ax=ax,
        node_color=[G.degree(n) for n in G.nodes()], cmap=plt.cm.viridis,
        node_size=[3000 * bc[n] + 100 for n in G.nodes()],
        edge_color="gray", alpha=0.8, with_labels=True, font_size=8)
plt.tight_layout()
plt.savefig("network.png", dpi=300, bbox_inches="tight")
plt.savefig("network.pdf", bbox_inches="tight")  # Vector format
print("Saved network.png and network.pdf")

import networkx as nx

# Erdos-Renyi random graph: n nodes, edge probability p
G_er = nx.erdos_renyi_graph(n=200, p=0.05, seed=42)
print(f"ER: {G_er.number_of_nodes()} nodes, {G_er.number_of_edges()} edges")

# Barabasi-Albert scale-free (power-law degree distribution)
G_ba = nx.barabasi_albert_graph(n=200, m=3, seed=42)

# Watts-Strogatz small-world
G_ws = nx.watts_strogatz_graph(n=200, k=6, p=0.1, seed=42)
print(f"WS clustering: {nx.average_clustering(G_ws):.3f}")

# Stochastic block model (community structure)
sizes, probs = [50, 50, 50], [[0.25,0.05,0.02],[0.05,0.35,0.07],[0.02,0.07,0.40]]
G_sbm = nx.stochastic_block_model(sizes, probs, seed=42)

# Built-in datasets and classic graphs
G_karate = nx.karate_club_graph()       # Zachary's karate club
G_grid = nx.grid_2d_graph(5, 7)         # 2D lattice
G_tree = nx.random_tree(n=50, seed=42)  # Random tree
G_geo = nx.random_geometric_graph(n=100, radius=0.2, seed=42)
# See references/algorithms_generators.md for full generator catalog

import networkx as nx
G = nx.Graph(name="example")              # Graph-level attribute
G.add_node(1, label="hub", weight=1.5)    # Node attributes
G.add_edge(1, 2, weight=0.8, type="ppi")  # Edge attributes

# Bulk set/get
nx.set_node_attributes(G, {1: "red", 2: "blue"}, "color")
colors = nx.get_node_attributes(G, "color")  # {1: 'red', 2: 'blue'}

import networkx as nx
import matplotlib.pyplot as plt
from networkx.algorithms import community

# Step 1: Load network and basic stats
G = nx.karate_club_graph()
print(f"Network: {G.number_of_nodes()} actors, {G.number_of_edges()} ties")
print(f"Density: {nx.density(G):.4f}, Clustering: {nx.average_clustering(G):.4f}")

# Step 2: Identify influential nodes
bc = nx.betweenness_centrality(G)
top_bc = sorted(bc.items(), key=lambda x: x[1], reverse=True)[:5]
print("Top 5 by betweenness:", [(n, f"{s:.3f}") for n, s in top_bc])

# Step 3: Detect communities
comms = community.greedy_modularity_communities(G)
print(f"Communities: {len(comms)}, modularity: {community.modularity(G, comms):.4f}")

# Step 4: Visualize with community coloring
pos = nx.spring_layout(G, seed=42)
fig, ax = plt.subplots(figsize=(10, 8))
for i, comm in enumerate(comms):
    nx.draw_networkx_nodes(G, pos, nodelist=list(comm), ax=ax,
                           node_color=[plt.cm.Set2(i)]*len(comm), node_size=400)
nx.draw_networkx_edges(G, pos, ax=ax, alpha=0.3)
nx.draw_networkx_labels(G, pos, ax=ax, font_size=8)
plt.axis("off")
plt.tight_layout()
plt.savefig("social_network_analysis.png", dpi=300, bbox_inches="tight")
print("Saved social_network_analysis.png")

import networkx as nx
import pandas as pd

# Step 1: Load interaction data from DataFrame
interactions = pd.DataFrame({
    "protein_a": ["TP53","TP53","BRCA1","BRCA1","MDM2","ATM","ATM","CHEK2","RB1","CDK2"],
    "protein_b": ["MDM2","BRCA1","ATM","CHEK2","RB1","CHEK2","BRCA2","CDC25A","CDK2","CCNA2"],
    "score": [0.99, 0.95, 0.92, 0.88, 0.91, 0.97, 0.85, 0.90, 0.87, 0.93]
})
G = nx.from_pandas_edgelist(interactions, "protein_a", "protein_b",
                             edge_attr="score")
print(f"PPI network: {G.number_of_nodes()} proteins, {G.number_of_edges()} interactions")

# Step 2: Network statistics
print(f"Connected: {nx.is_connected(G)}")
print(f"Diameter: {nx.diameter(G)}")
print(f"Avg path length: {nx.average_shortest_path_length(G):.2f}")
print(f"Transitivity: {nx.transitivity(G):.4f}")

# Step 3: Hub identification (multiple centrality measures)
degree_c = nx.degree_centrality(G)
between_c = nx.betweenness_centrality(G)
close_c = nx.closeness_centrality(G)

results = pd.DataFrame({
    "protein": list(G.nodes()),
    "degree_centrality": [degree_c[n] for n in G.nodes()],
    "betweenness": [between_c[n] for n in G.nodes()],
    "closeness": [close_c[n] for n in G.nodes()],
}).sort_values("betweenness", ascending=False)
print("\nHub proteins:")
print(results.head(5).to_string(index=False))

# Step 4: Export for downstream analysis
nx.write_graphml(G, "ppi_network.graphml")
results.to_csv("protein_centrality.csv", index=False)
print("Exported ppi_network.graphml and protein_centrality.csv")

import networkx as nx

# Create weighted graph
G = nx.erdos_renyi_graph(50, 0.15, seed=42)
for u, v in G.edges():
    G[u][v]["weight"] = round(nx.utils.py_random_state(42).random(), 2)

mst = nx.minimum_spanning_tree(G, weight="weight")
print(f"Original: {G.number_of_edges()} edges")
print(f"MST: {mst.number_of_edges()} edges")
total_weight = sum(d["weight"] for _, _, d in mst.edges(data=True))
print(f"MST total weight: {total_weight:.2f}")

import networkx as nx

G = nx.karate_club_graph()

# Find all maximal cliques
cliques = list(nx.find_cliques(G))
print(f"Maximal cliques: {len(cliques)}")
largest_clique = max(cliques, key=len)
print(f"Largest clique size: {len(largest_clique)}, nodes: {largest_clique}")

# Greedy graph coloring
coloring = nx.greedy_color(G, strategy="largest_first")
n_colors = max(coloring.values()) + 1
print(f"Chromatic number (greedy upper bound): {n_colors}")

import networkx as nx

# Task dependency DAG
D = nx.DiGraph()
D.add_edges_from([
    ("download_data", "preprocess"),
    ("download_data", "validate"),
    ("preprocess", "analyze"),
    ("validate", "analyze"),
    ("analyze", "visualize"),
    ("analyze", "report"),
    ("visualize", "report"),
])

print(f"Is DAG: {nx.is_directed_acyclic_graph(D)}")
order = list(nx.topological_sort(D))
print(f"Execution order: {order}")

# Find all paths from start to end
paths = list(nx.all_simple_paths(D, "download_data", "report"))
print(f"Paths to report: {len(paths)}")
for p in paths:
    print(f"  {' -> '.join(p)}")