@inproceedings{e2b54a276991468d96f74ce355532e35,
title = "DESTINE: Dense Subgraph Detection on Multi-Layered Networks",
abstract = "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.",
keywords = "dense subgraph detection, multi-layered network",
author = "Zhe Xu and Si Zhang and Yinglong Xia and Liang Xiong and Jiejun Xu and Hanghang Tong",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 30th ACM International Conference on Information and Knowledge Management, CIKM 2021 ; Conference date: 01-11-2021 Through 05-11-2021",
year = "2021",
month = oct,
day = "26",
doi = "10.1145/3459637.3482083",
language = "English (US)",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "3558--3562",
booktitle = "CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management",
address = "United States",
}