Year: 2025
Type: article
Abstract: This paper proposes efficient solutions for k -core decomposition with high parallelism. The problem of k -core decomposition is fundamental in graph analysis and has applications across various domains. However, existing algorithms face significant challenges in achieving work-efficiency in theory and/or high parallelism in practice, and suffer from various performance bottlenecks. We present a simple, work-efficient parallel framework for k -core decomposition that is easy to implement and adaptable to various strategies for improving work-efficiency. We introduce two techniques to enhance parallelism: a sampling scheme to reduce contention on high-degree vertices, and vertical granularity control (VGC) to mitigate scheduling overhead for low-degree vertices. Furthermore, we design a hierarchical bucket structure to optimize performance for graphs with high coreness values. We evaluate our algorithm on a diverse set of real-world and synthetic graphs. Compared to state-of-the-art parallel algorithms, including ParK, PKC, and Julienne, our approach demonstrates superior performance on 23 out of 25 graphs when tested on a 96-core machine. Our algorithm shows speedups of up to 315× over ParK, 33.4× over PKC, and 52.5× over Julienne.
Authors: Youzhe Liu, Xiaojun Dong, Yan Gu, Yihan Sun
Institution: University of California, Riverside
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: CAREER: Efficient Algorithms for Modern Computer Architecture, III: Small: Rethinking the Data Organization and Lifecycle in LSM Storage Systems, G6671297155, G7598007753, CAREER: Parallel Algorithms: Theory for Practice, AF: Small: New Directions for Parallel Data Structures, NSF POSE: Phase II: Open-Source Ecosystem for OpenCilk
OpenAlex ID: W4411403515