graphina 0.2.0

A graph data science library for Rust
Documentation
graphina-0.2.0 has been yanked.

Graphina

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Graphina is a graph data science library for Rust. It provides the common data structures and algorithms used for analyzing the graphs of real-world networks such as social, transportation, and biological networks.

Compared to other Rust graph libraries like petgraph and rustworkx, Graphina aims to provide a more high-level API and a wide range of ready-to-use algorithms for network analysis and graph mining tasks.

[!IMPORTANT] Graphina is in the early stages of development, so breaking changes may occur.

Structure

Graphina consists of two main parts: a core library and extensions. The core library provides the basic data structures and algorithms for working with graphs. The extensions are modules outside the core library that contain more advanced algorithms for specific tasks like community detection, link prediction, and calculating node and edge centrality scores.

The extensions are designed to be independent of each other, and depend on the core library for the basic graph operations.

Graphina Core

Module Features/Algorithms Status Notes
Types Directed and undirected graphsWeighted and unweighted graphs Tested Core graph types
Exceptions List of exceptions Tested Custom error types for Graphina
IO Edge listAdjacency list Tested I/O routines for reading/writing graph data
Generators Erdős–Rényi graphWatts–Strogatz graphBarabási–Albert graphComplete graphBipartite graphStar graphCycle graph Tested Graph generators for random and structured graphs
Paths Dijkstra’s algorithmBellman–Ford algorithmFloyd–Warshall algorithmJohnson’s algorithmA* search algorithmIterative deepening A* Tested Shortest paths algorithms
MST Prim’s algorithmKruskal’s algorithmBorůvka’s algorithm Tested Minimum spanning tree algorithms
Traversal Breadth-first search (BFS)Depth-first search (DFS)Iterative deepening DFSBidirectional search Tested Graph traversal algorithms

Extensions

Module Features/Algorithms Status Notes
Centrality Degree centralityCloseness centralityBetweenness centralityEigenvector centralityPageRank centralityKatz centralityHarmonic centralityLocal/global reaching centralityVoteRank centralityLaplacian centrality Centrality measures
Links Resource allocation indexJaccard coefficientAdamic–Adar indexPreferential attachmentCN Soundarajan–HopcroftRA Index Soundarajan–HopcroftWithin–inter-cluster ratioCommon neighbor centrality Link prediction algorithms
Community Label PropagationLouvain MethodGirvan–Newman algorithmSpectral ClusteringPersonalized PageRankInfomapConnected components Community detection and clustering algorithms
Approximation Local node connectivity (BFS-based)Maximum independent set (greedy with neighbor caching)Maximum clique (greedy heuristic)Clique removalLarge clique sizeAverage clustering coefficientDensest subgraph (greedy peeling)Diameter lower boundMinimum weighted vertex cover (greedy re‑evaluated)Minimum maximal matching (greedy)Approximate Ramsey R2TSP approximations (greedy, simulated annealing, threshold accepting, Christofides placeholder)Treewidth decompositions (min degree, min fill-in) Approximations and heuristic methods for NP‑hard problems

[!NOTE] Status shows whether the module is tested and benchmarked. Empty status means the module is implemented but not tested and benchmarked yet.

Installation

cargo add graphina

Graphina requires Rust 1.83 or later.

Documentation

See the docs for the latest documentation.

Check out the docs.rs/graphina for the latest API documentation.

Contributing

See CONTRIBUTING.md for details on how to make a contribution.

License

This project is licensed under either of these: