Analyze 4 distinct graph types using advanced algorithms like PageRank. Optimize your Python graph data structures today.
Graph Theory
Python
Data Science
Network Analysis
Algorithms
About This Skill
NetworkX is a powerful Python package for analyzing complex networks. It supports 4 main graph types—Graph, DiGraph, MultiGraph, and MultiDiGraph—enabling developers to implement standard algorithms like Dijkstra's and PageRank with ease.
Quick Start
1Install networkx via pip
2Import networkx as nx
3Create a graph and add nodes or edges
Example Command
import networkx as nx; G = nx.Graph(); G.add_edge(1, 2)
Core Capabilities
Graph Creation and Manipulation
Support for 4 graph types including directed, undirected, and multi-edge graphs with custom attributes.
Execute standard algorithms like Dijkstra's, PageRank, minimum spanning trees, and maximum flow calculations.
Network Generation
Create synthetic networks using random, scale-free, or small-world models for testing and simulation.
Usage Examples
Input
Find the shortest path between node A and B in a directed graph.
Output
nx.shortest_path(G, source='A', target='B')
Input
Calculate the PageRank of all nodes in a social network graph.
Output
nx.pagerank(G)
Before
Manually iterating through a list of tuples to find connections and building a dictionary map.
After
Using nx.from_edgelist() to instantly create a queryable graph object with built-in analysis methods.
SKILL.md
---
name: networkx
description: "NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs."
license: 3-clause BSD license
metadata:
skill-author: K-Dense Inc.
risk: unknown
source: "https://github.com/networkx/networkx"
---
# NetworkX
## Overview
NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs. Use this skill when working with network or graph data structures, including social networks, biological networks, transportation systems, citation networks, knowledge graphs, or any system involving relationships between entities.
## When to Use This Skill
Invoke this skill when tasks involve:
- **Creating graphs**: Building network structures from data, adding nodes and edges with attributes
- **Graph analysis**: Computing centrality measures, finding shortest paths, detecting communities, measuring clustering
- **Graph algorithms**: Running standard algorithms like Dijkstra's, PageRank, minimum spanning trees, maximum flow
- **Network generation**: Creating synthetic networks (random, scale-free, small-world models) for testing or simulation
- **Graph I/O**: Reading from or writing to various formats (edge lists, GraphML, JSON, CSV, adjacency matrices)
- **Visualization**: Drawing and customizing network visualizations with matplotlib or interactive libraries
- **Network comparison**: Checking isomorphism, computing graph metrics, analyzing structural properties
## Core Capabilities
### 1. Graph Creation and Manipulation
NetworkX supports four main graph types:
- **Graph**: Undirected graphs with single edges
- **DiGraph**: Directed graphs with one-way connections
- **MultiGraph**: Undirected graphs allowing multiple edges between nodes
- **MultiDiGraph**: Directed graphs with multiple edges
Create graphs by:
```python
import networkx as nx
# Create empty graph
G = nx.Graph()
# Add nodes (can be any hashable type)
G.add_node(1)
G.add_nodes_from([2, 3, 4])
G.add_node
Frequently Asked Questions
FAQ
Is NetworkX compatible with other Python data science tools?
Yes, it integrates seamlessly with Matplotlib for visualization, NumPy for matrix operations, and pandas for data frame conversions.
Who is the target audience for this skill?
Python developers, data scientists, and researchers working with relational data, social networks, or biological systems.
How does NetworkX differ from alternatives like igraph?
NetworkX is written in pure Python, making it highly flexible and easy to install, whereas igraph is C-based and may offer better performance for extremely large graphs.
What programming languages are supported?
This skill specifically supports Python, as NetworkX is a native Python package.
What results can I expect when using this skill?
You can expect efficient modeling of complex relationships and deep structural insights through standard graph metrics and algorithms.
Discussion
Discussion
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Trigger Phrases
Use these phrases to activate this skill in your AI coding assistant:
create a directed graphcalculate shortest pathdetect communities in networkgenerate random graphvisualize networkx graph
