PiTruss Community Search for Multilayer Graphs

Run-An Wang, Zhaonian Zou, Dandan Liu, Xudong Liu

Abstract: Community search on multilayer graphs has significant applications in fields such as bioinformatics, social network analysis, and financial fraud detection, offering deeper insights compared to traditional community search on single-layer graphs. However, existing approaches often suffer from several key limitations, including inefficiency and a lack of flexibility in accommodating query requirements. To address these challenges, we investigate the problem of community search over large multilayer graphs. Specifically, we introduce a novel multilayer community model called PivotTruss Community (PiTC) with provably nice structural guarantees. We formalize the PiTC search (PiTCS) problem, which aims to efficiently identify personalized PiTCs for a given query vertex. To solve the PiTCS problem, we propose an efficient algorithm and design an elegant index to accelerate the search process. In addition, we propose a parameter recommendation method to improve the usability of PiTCS. To further optimize performance, we introduce a method to compact the index by making a trade-off between search time and index size. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our proposed algorithms.

External IDs:dblp:journals/tkde/WangZLL25a