GraphCSR: A Space and Time-Efficient Sparse Matrix Representation for Web-scale Graph Processing

Xinbiao Gan, Tiejun Li, Qiang Zhang, Liyang Wu, Bo Yang, Chunye Gong, Jie Liu, KAI LU

Track: Systems and infrastructure for Web, mobile, and WoT

Keywords: Graph representation; CSR, Sorted graph, Graph processing

TL;DR: GraphCSR: A Space and Time-Efficient Sparse Matrix Representation for Web-scale Graph Processing

Abstract:

Graph data processing is essential for web-scale applications, including social networks, recommendation systems, and web of things
(WoT) systems, where large, sparsely connected graphs dominate. Traditional sparse matrix storage formats like compressed sparse
row (CSR) face significant memory and performance bottlenecks in distributed, federated, and edge-based computing environments,
which are increasingly central to the web. To address this challenge, we propose GraphCSR, a novel storage format that clusters ver-
tices with identical edge degrees and stores only the starting index of each group. This approach minimizes memory overhead and
facilitates batch memory access while enhancing overall performance, making it particularly suitable for federated systems and
resource-constrained edge nodes. Our experiments across various graph operations and large datasets show that GraphCSR achieves
considerable memory savings and performance gains of large-scale, distributed graph processing. When deployed GraphCSR on a
production-grade supercomputer with 79,024 computing nodes, it outperforms the top-ranked system on the Graph 500 list, demon-
strating its potential for scaling web and WoT graph processing in large-scale distributed computing systems.

Submission Number: 310