Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective
Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective
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DOI：
10.1145/3056562
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发表时间：
2017-08-01
影响因子：
3.6
通讯作者：
He, Qing
中科院分区：
文献类型：
--
作者：
Chen, Chen;Tong, Hanghang;He, Qing
日益互联的世界促使了不同领域的网络融合，从而催生了一种新的网络模型--多层网络。这种网络系统的示例包括关键基础设施网络、生物系统、组织级协作、跨平台电子商务等。跨层依赖性是多层网络区别于其他网络模型的一个关键结构，它描述了不同层节点之间的关联。毋庸置疑，网络中的跨层依赖性在许多数据挖掘应用中起着至关重要的作用，如系统鲁棒性分析和复杂网络控制。然而，由于噪声、有限的可访问性等原因，了解确切的依赖关系仍然是一项艰巨的任务。在本文中，我们通过将其建模为集体协作过滤问题来解决跨层依赖推理问题。基于这一思想，我们提出了一个有效的算法FASCINATE，可以揭示未观察到的线性复杂度的依赖。此外，我们推导出FASCINATE-ZERO，它是FASCINATE的一个在线变体，可以通过检查其邻域依赖关系来及时响应新添加的节点。我们对真实的数据集进行了广泛的评估，以证实我们所提出的方法的优越性。
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 FASCINATE that can reveal unobserved dependencies with linear complexity. Moreover, we derive FASCINATE-ZERO, an online variant of FASCINATE 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.