"High-Performance Truss Analytics in Arkouda" by Zhihui Du, Joseph Patchett et al.
 

High-Performance Truss Analytics in Arkouda

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

In graph analytics, a truss is a cohesive subgraph based on the number of triangles supporting each edge. It is widely used for community detection applications such as social networks and security analysis, and the performance of truss analytics highly depends on its triangle counting method. This paper proposes a novel triangle counting kernel named Minimum Search (MS). Minimum Search can select two smaller adjacency lists out of three and uses fine-grained parallelism to improve the performance of triangle counting. Then, two basic algorithms, MS-based triangle counting, and MS-based support updating are developed. Based on the novel triangle counting kernel and the two basic algorithms above, three fundamental parallel truss analytics algorithms are designed and implemented to enable different kinds of graph truss analysis. These truss algorithms include an optimized K-Truss algorithm, a Max-Truss algorithm, and a Truss Decomposition algorithm. Moreover, all proposed algorithms have been implemented in the parallel language Chapel and integrated into an open-source framework, Arkouda. Through Arkouda, data scientists can efficiently con-duct graph analysis through an easy-to-use Python interface and handle large-scale graph data in powerful back-end computing resources. Experimental results show that the proposed methods can significantly improve the performance of truss analysis on real-world graphs compared with the existing and widely adopted list intersection-based method. The implemented code is publicly available from GitHub (https://github.com/Bears-R-Us/arkouda-njit).

Identifier

85158144691 (Scopus)

ISBN

[9781665494236]

Publication Title

Proceedings 2022 IEEE 29th International Conference on High Performance Computing Data and Analytics Hipc 2022

External Full Text Location

https://doi.org/10.1109/HiPC56025.2022.00026

First Page

105

Last Page

114

Grant

CCF-2109988

Fund Ref

National Science Foundation

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