Research

Dense subgraph detection is a fundamental building block for a variety of applications. Most of the existing methods aim to discover dense subgraphs within either a single network or a multi-view network while ignoring the informative node dependencies across multiple layers of networks in a complex system. To date, it largely remains a daunting task to detect dense subgraphs on multi-layered networks. In this paper, we formulate the problem of dense subgraph detection on multi-layered networks based on cross-layer consistency principle. We further propose a novel algorithm DESTINE based on projected gradient descent with the following advantages. First, armed with the cross-layer dependencies, DESTINE is able to detect significantly more accurate and meaningful dense subgraphs at each layer. Second, it scales linearly w.r.t. the number of links in the multi-layered network. Extensive experiments demonstrate the efficacy of the proposed DESTINE algorithm in various cases.