A Parallel and Distributed Rust Library for Core Decomposition on Large Graphs

TL;DR

This paper presents a Rust-based parallel and distributed library for core decomposition on large graphs, achieving significant speedups over sequential and Python implementations through optimized multi-threaded algorithms.

Contribution

It adapts a distributed k-core algorithm for shared-memory systems in Rust, introducing three optimized versions that improve performance and scalability on large graph datasets.

Findings

Up to 11x speedup on 16 threads

Execution times up to 100x faster than Python implementation

Effective parallelization for large-scale graph analysis

Abstract

In this paper, we investigate the parallelization of $k$-core decomposition, a method used in graph analysis to identify cohesive substructures and assess node centrality. Although efficient sequential algorithms exist for this task, the scale of modern networks requires faster, multicore-ready approaches. To this end, we adapt a distributed $k$-core algorithm originally proposed by Montresor et al. to shared-memory systems and implement it in Rust, leveraging the language's strengths in concurrency and memory safety. We developed three progressively optimized versions: SequentialK as a baseline, ParallelK introducing multi-threaded message passing, and FastK further reducing synchronization overhead. Extensive experiments on real-world datasets, including road networks, web graphs, and social networks, show that FastK consistently outperforms both SequentialK and ParallelK, as well as a reference Python implementation available in the NetworkX library. Results indicate up to an 11x speedup on 16 threads and execution times up to two orders of magnitude faster than the Python implementation.