---
name: networkx-social-d3ed361114c7
description: "Social network and graph analysis via NetworkX. Use when: graph analysis, community detection, centrality measures, network visualization, knowledge graphs, bipartite networks, graph I/O. NOT for: large-scale graph processing (>1M nodes), GPU graph analytics (use cuGraph)."
metadata: { "openclaw": { "emoji": "🕸️", "requires": { "bins": ["python3"] }, "install": [{ "id": "uv-networkx", "kind": "uv", "package": "networkx matplotlib" }] } }
---

# NetworkX Graph Analysis

Graph analysis, community detection, centrality measures, knowledge graphs, bipartite networks, and visualization.

## Graph Creation

```python
import networkx as nx

G = nx.Graph()
G.add_edge('Alice', 'Bob', weight=3)
G.add_edges_from([('Bob', 'Carol'), ('Carol', 'Dave'), ('Alice', 'Dave')])

D = nx.DiGraph()                          # directed graph
G = nx.read_edgelist('network.txt')       # from edge list file
G = nx.from_pandas_edgelist(df, 'source', 'target')  # from DataFrame
```

## Knowledge Graph Construction

```python
# Build a knowledge graph from (subject, predicate, object) triples
KG = nx.DiGraph()
triples = [
    ("Python", "is_a", "Language"), ("Pandas", "depends_on", "Python"),
    ("NumPy", "depends_on", "Python"), ("Pandas", "depends_on", "NumPy"),
]
for subj, pred, obj in triples:
    KG.add_edge(subj, obj, relation=pred)

# Query: all dependencies of Pandas
deps = list(nx.descendants(KG, "Pandas"))

# Subgraph around a node (ego graph)
ego = nx.ego_graph(KG, "Python", radius=2, undirected=True)
```

## Centrality Measures

```python
dc = nx.degree_centrality(G)              # fraction of connected nodes
bc = nx.betweenness_centrality(G)         # shortest-path intermediary
cc = nx.closeness_centrality(G)           # inverse avg distance
ec = nx.eigenvector_centrality(G, max_iter=1000)  # neighbor importance
pr = nx.pagerank(D, alpha=0.85)           # PageRank (directed)

for node, score in sorted(bc.items(), key=lambda x: -x[1])[:5]:
    print(f"{node}: {score:.4f}")
```

## Community Detection

```python
from networkx.algorithms.community import louvain_communities, modularity
from networkx.algorithms.community import label_propagation_communities, greedy_modularity_communities

communities = louvain_communities(G, seed=42)        # modularity optimization
communities = list(label_propagation_communities(G))  # fast, non-deterministic
greedy = list(greedy_modularity_communities(G))       # greedy modularity

mod = modularity(G, communities)
print(f"Modularity: {mod:.4f}")
```

## Shortest Paths and Distances

```python
path = nx.shortest_path(G, source='Alice', target='Dave')
length = nx.shortest_path_length(G, source='Alice', target='Dave')
if nx.is_connected(G):
    diameter = nx.diameter(G)
    avg_path = nx.average_shortest_path_length(G)
```

## Clustering and Connectivity

```python
avg_cc = nx.average_clustering(G)
transitivity = nx.transitivity(G)                     # global clustering
components = list(nx.connected_components(G))
largest_cc = G.subgraph(max(components, key=len)).copy()
core_numbers = nx.core_number(G)
```

## Bipartite Graphs and Projections

```python
from networkx.algorithms import bipartite

B = nx.Graph()
B.add_nodes_from(["u1", "u2", "u3"], bipartite=0)    # users
B.add_nodes_from(["p1", "p2"], bipartite=1)           # products
B.add_edges_from([("u1", "p1"), ("u2", "p1"), ("u2", "p2"), ("u3", "p2")])
users = {n for n, d in B.nodes(data=True) if d["bipartite"] == 0}
user_graph = bipartite.projected_graph(B, users)      # shared neighbors become edges
weighted_proj = bipartite.weighted_projected_graph(B, users)
```

## Network Visualization

```python
import matplotlib.pyplot as plt

pos = nx.spring_layout(G, seed=42, k=1.5)
node_sizes = [3000 * dc[n] for n in G.nodes()]
color_map = {n: i for i, comm in enumerate(communities) for n in comm}
nx.draw_networkx(G, pos, node_size=node_sizes,
                 node_color=[color_map.get(n, 0) for n in G.nodes()],
                 cmap=plt.cm.Set3, edge_color='gray', alpha=0.8, font_size=9)
plt.tight_layout()
plt.savefig('network.png', dpi=150)
plt.close()

# Other layouts: nx.circular_layout, nx.kamada_kawai_layout, nx.shell_layout
```

## Graph I/O

```python
# GraphML (XML-based, preserves attributes)
nx.write_graphml(G, 'graph.graphml')
G = nx.read_graphml('graph.graphml')

# GEXF (Gephi format)
nx.write_gexf(G, 'graph.gexf')
G = nx.read_gexf('graph.gexf')

# JSON (node-link format)
from networkx.readwrite import json_graph
import json
data = json_graph.node_link_data(G)
json.dump(data, open('graph.json', 'w'))
G = json_graph.node_link_graph(json.load(open('graph.json')))
```

## Best Practices

1. Use `G.copy()` before destructive operations (node/edge removal).
2. For large graphs, prefer `louvain_communities` over `girvan_newman`.
3. Set `seed` in layout functions for reproducible visualizations.
4. Check `nx.is_connected(G)` before computing diameter or avg path length.
5. For weighted networks, pass `weight='weight'` to centrality functions.
6. For knowledge graphs, use `DiGraph` and store relation types as edge attributes.
7. Export to GraphML or GEXF for interoperability with Gephi and other tools.


---
Source attribution: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/networkx-social/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Peter Steinberger

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/networkx-social/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics
Roles: Data Analyst, Data Scientist, Market Research Analyst
Specialties: API, Backend
Tools: Matplotlib, Networkx
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: ae3d1308177162e435cf8218be60adab1fcaa328073c2e16daed8f7c0b1a42f9