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Knowl. Discov. Data"],"published-print":{"date-parts":[[2017,11,30]]},"abstract":"<jats:p>The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model\u2014multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm F&lt;scp;&gt;ascinate&lt;\/scp;&gt; that can reveal unobserved dependencies with linear complexity. Moreover, we derive F&lt;scp;&gt;ascinate&lt;\/scp;&gt;-ZERO, an online variant of F&lt;scp;&gt;ascinate&lt;\/scp;&gt; that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.<\/jats:p>","DOI":"10.1145\/3056562","type":"journal-article","created":{"date-parts":[[2017,6,30]],"date-time":"2017-06-30T12:36:19Z","timestamp":1498826179000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Cross-Dependency Inference in Multi-Layered Networks"],"prefix":"10.1145","volume":"11","author":[{"given":"Chen","family":"Chen","sequence":"first","affiliation":[{"name":"Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanghang","family":"Tong","sequence":"additional","affiliation":[{"name":"Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[{"name":"City University of New York, Park Ave. New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Ying","sequence":"additional","affiliation":[{"name":"Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"University at Buffalo, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,6,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.89.032804"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2011.103"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 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Buldyrev, Roni Parshani, Gerald Paul, H. Eugene Stanley, and Shlomo Havlin. 2010. Catastrophic cascade of failures in interdependent networks. 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