A high-performance Rust library for graph algorithms, built on a parallel CSR data structure for large-scale graphs.
Graph is a Rust library that provides a collection of high-performance graph algorithms for large-scale graphs. It solves the problem of efficiently processing graphs with billions of nodes and edges by using a parallel Compressed-Sparse-Row (CSR) data structure and optimized algorithm implementations.
Developers and data scientists working with large-scale graph data, such as those in network analysis, recommendation systems, or social network analysis, who need performant graph algorithms in Rust.
Developers choose Graph for its focus on performance and scalability, leveraging parallelism and efficient data structures to handle massive graphs, along with a type-safe API and support from Neo4j Labs expertise.
A library for high-performant graph algorithms.
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Uses Rayon to parallelize all steps during graph creation, significantly improving performance for large datasets as stated in the README.
Implements a Compressed-Sparse-Row format optimized for fast, concurrent access, enabling efficient handling of billions of nodes and edges.
Algorithms like Page Rank are engineered to run efficiently on massive graphs, addressing real-world large-scale problems with optimized implementations.
Provides type-safe graph creation and input handling, reducing errors and ensuring correctness, as demonstrated in the builder examples.
The README primarily showcases Page Rank, indicating a narrow set of built-in algorithms compared to more comprehensive graph libraries.
Based on the CSR data structure, the library is best for static graphs, making it unsuitable for dynamic applications requiring frequent updates.
While a Python package (graph-mate) exists, the core library is Rust-only, limiting accessibility for developers in other language ecosystems without custom bindings.