networkx
Category: data-science-and-ml · License: 3-clause BSD license · Version: 1.2
Tags: network analysis, graph theory, graphs, community detection, Python
When to use it
Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.Example requests
Ask your agent in plain language, for example:- “How do I get the top 10 nodes by betweenness in my graph?”
- “I have an edge list in interactions.tsv (columns: protein_a, protein_b, score) from STRING for human PPI. I loaded it with nx.read_edgelist and now get ‘TypeError: Failed to convert edge data’ on the score column. Fix this, then find the connected components, report the size of the largest one, and run Louvain community detection with a fixed seed.”
- “I’m modeling how a rumor spreads through a school. I want to simulate a few hundred students where most are in tight friend clusters but a few have friends across clusters, then see how many hops it takes on average for any two students to connect. How should I set this up in Python?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.