Interrelated Dense Pattern Detection in Multilayer Networks

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 (such as bipartite graph, multi-view network) but remain under-explored on 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. In this paper, we propose InDuen, a novel algorithm designed to detect interrelated densest subgraphs in multilayer networks through joint optimization of coupled factorization and local search for an elaborate-designed joint density measure. It is (a) effective for both large synthetic and real networks, (b) resistant to camouflage for anomaly detection, and (c) linearly scalable. Experimental results demonstrate that InDuen outperforms the state-of-the-art baselines in accurately detecting interrelated densest subgraphs under various settings. Furthermore, InDuen uncovers some intriguing patterns in real-world data, i.e., closely cooperated academic groups and interrelated dependent functional components in biology-net. InDuen achieves more than $35 \times$ speedup compared to the SOTA method Destine.