Interrelated Dense Pattern Detection in Multilayer Networks (Extended Abstract)
Wenjie Feng, Wang Li, Bryan Hooi, See Kiong Ng, Shenhua Liu · 2025
Given a heterogeneous multilayer network with various connections in pharmacology, how can we detect components with intensive interactions and strong dependencies? Can we accurately capture suspicious groups in a multi-lot transaction network under camouflage? These challenges related to dense subgraph detection have been extensively studied in simple graphs but remain under-explored in complex networks. Existing methods struggle to effectively handle the intricate dependencies, let alone accurately identify the interrelated dense connected patterns within a series of complex heterogeneous networks. Here, we introduce INDUEN, a novel algorithm designed to detect interrelated densest subgraphs in multilayer networks by leveraging joint optimization of coupled factorization and local search for an elaborate-designed joint density measure. Experimental results demonstrate that INDUEN outperforms the state-of-the-art baselines in accurately detecting interrelated densest sub graphs under various settings. Furthermore, INDUEN uncovers some intriguing patterns in real-world data; it is linearly scalable and achieves more than 35 × speedup compared to the state-of-the-art method Destine.