| Title | Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective |
| Publication Type | Journal Article |
| Year of Publication | 2017 |
| Authors | Chen, Chen, Tong, Hanghang, Xie, Lei, Ying, Lei, He, Qing |
| Journal | ACM Trans. Knowl. Discov. Data |
| Volume | 11 |
| Pagination | 42:1–42:26 |
| ISSN | 1556-4681 |
| Keywords | composability, Cross Layer Security, cross-layer dependency, graph mining, Multi-layered network, pubcrawl, Resiliency |
| Abstract | The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model—multi-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\textbackslashtextlessscp;\textbackslashtextgreaterascinate\textbackslashtextless/scp;\textbackslashtextgreater that can reveal unobserved dependencies with linear complexity. Moreover, we derive F\textbackslashtextlessscp;\textbackslashtextgreaterascinate\textbackslashtextless/scp;\textbackslashtextgreater-ZERO, an online variant of F\textbackslashtextlessscp;\textbackslashtextgreaterascinate\textbackslashtextless/scp;\textbackslashtextgreater 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. |
| URL | https://dl.acm.org/doi/10.1145/3056562 |
| DOI | 10.1145/3056562 |
| Citation Key | chen_cross-dependency_2017 |
Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective