Cross-Dependency Inference in Multi-Layered Networks

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 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.

A Local Algorithm for Structure-Preserving Graph Cut

“…Nowadays, large-scale networks data appear in a broad spectrum of disciplines, from social networks [21,22] to collaborative networks [8,9], from rare category detection [34][35][36][37] to crowdsourcing [38,39]. Local spectral clustering techniques provide a simple, efficient time alternative to recursively identify a local sparse cut C with an upperbounded conductance.…”

MultiC2: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity

“…Compared with the existing models, our proposed low-rank regularization model imposes a structural correlations of task and worker heterogeneities. This type of low-rank principle also has been widely applied in various domains, such as social [10,15] and recommendations [20]. Recently, there are also some works [24,28,27] that focus on designing and estimating the various abilities of the workers and learning to hire the workers that can learn over time.…”

Toward Personalized Relational Learning

“…Cora and Citeseer are real-world academic networks 3 while BlogCatalog is a social media network 4 . Cora: The Cora dataset is a citation network with 2708 publications and 5429 citations.…”

“…Conventional data mining and machine learning techniques cannot be easily applied to networks as they assume data instances are independently and identically distributed (i.i.d.). In reality, instances in a network are explicitly or implicitly correlated, with complex dependencies [4,27], making the enduring and deeply buried data i.i.d. assumption invalid.…”