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Title: DESTINE: Dense Subgraph Detection on Multi-Layered Networks
Dense subgraph detection is a fundamental building block for a va- riety 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 sub- graph detection on multi-layered networks based on cross-layer consistency principle. We further propose a novel algorithm Des- tine based on projected gradient descent with the following ad- vantages. 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 num- ber of links in the multi-layered network. Extensive experiments demonstrate the efficacy of the proposed Destine algorithm in various cases.
Authors:
; ; ; ; ;
Award ID(s):
1939725 1947135
Publication Date:
NSF-PAR ID:
10332510
Journal Name:
Destine: Dense Subgraph Detection on Multi-Layered Networks
Page Range or eLocation-ID:
3558 to 3562
Sponsoring Org:
National Science Foundation
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