network-science

Network science in Rust. Who's the most important node? What communities exist? How does information spread?

Features

• Graph structures — undirected and directed graphs with adjacency lists
• Centrality — degree, betweenness (Brandes 2001), closeness, eigenvector, PageRank
• Community detection — Louvain (Blondel et al. 2008), label propagation
• Epidemic models — SIR, SIS, epidemic thresholds
• Degree distributions — power-law MLE (Clauset–Shalizi–Newman), scale-free detection
• Network models — Erdős–Rényi, Barabási–Albert preferential attachment, Watts–Strogatz small world
• Small-world analysis — clustering coefficient, average path length, small-world coefficient
• Resilience — node/edge percolation, targeted attacks
• Assortativity — degree assortativity, mixing matrices
• Social networks — agent-based network modeling, community summaries, comparison

Quick start

[dependencies]
network-science = "0.1"

use network_science::{Graph, degree_centrality, clustering_coefficient};

let mut g = Graph::new();
g.add_edge(0, 1);
g.add_edge(1, 2);
g.add_edge(2, 0);

let dc = degree_centrality(&g);
let cc = clustering_coefficient(&g);

License

MIT OR Apache-2.0