FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks
Chen Chen*, Arizona State Unversity; Hanghang Tong, Arizona State University; Lei Xie, City University of New York; Lei YIng, Arizona State University; Qing He, Arizona State University
Multi-layered networks have recently emerged as a new network model, which naturally ﬁnds itself in many high-impact application domains, ranging from critical inter-dependent infrastructure networks, biological systems, organization-level collaborations, to cross-platform e-commerce, etc. Cross-layer dependency, which describes the dependencies or the associations between nodes across different layers/networks, often plays a central role in many data mining tasks on such multi-layered networks. Yet, it remains a daunting task to accurately know the cross-layer dependency a prior. In this paper, we address the problem of inferring the missing cross-layer dependencies on multi-layered networks. The key idea behind our method is to view it as a collective collaborative ﬁltering problem. By formulating the problem into a regularized optimization model, we propose an effective algorithm to ﬁnd the local optima with linear complexity. Furthermore, we derive an online algorithm to accommodate newly arrived nodes, whose complexity is just linear wrt the size of the neighborhood of the new node. We perform extensive empirical evaluations to demonstrate the effectiveness and the efﬁciency of the proposed methods.
Filed under: Graph Mining and Social Networks