Dense subgraph mining kmax-truss optimization algorithm

Ze Zhang; Long Ren; Yang You; Kun Zhang; Jianqiang Huang

doi:10.1117/12.2641426

10 November 2022 Dense subgraph mining kmax-truss optimization algorithm

Ze Zhang, Long Ren, Yang You, Kun Zhang, Jianqiang Huang

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 1234821 (2022) https://doi.org/10.1117/12.2641426
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

K-truss is a dense subgraph based on clique. Finding the largest k-truss in a graph is an important problem in graph analysis. The truss decomposition algorithm is widely used in finding k-truss because of its simplicity and ease of implementation. With the explosive growth of graph data scale, the traditional truss decomposition algorithm causes a lot of redundant computation and is no longer applicable. The kmax search strategy that combines large, medium and small steps, realizes subgraph prediction through k-core, reduces the data size, uses large, medium and small steps to retrieve, reduces the number of iterations, and eliminates some redundant calculations. However, the middle step and small step use fixed values, and the number of iterations is too many on some scale graphs. This paper proposes a dynamic adaptive step size method, and uses the clique to analyze the intermediate results, ending the iterative process early and improving the execution efficiency. The experimental results show that compared with the existing kmax search strategy combining large, medium and small steps, 30% iterations can be reduced.

Citation Download Citation

Ze Zhang, Long Ren, Yang You, Kun Zhang, and Jianqiang Huang "Dense subgraph mining kmax-truss optimization algorithm", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 1234821 (10 November 2022); https://doi.org/10.1117/12.2641426

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