文章基本信息

标题：Link Prediction through Deep Generative Model
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作者：Xu-Wen Wang ; Yize Chen ; Yang-Yu Liu 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2020
卷号：23
期号：10
页码：1-53
DOI：10.1016/j.isci.2020.101626
语种：English
出版社：Elsevier
摘要：SummaryInferring missing links based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine, e-commerce, social media, and criminal intelligence. Numerous methods have been proposed to solve the link prediction problem. Yet, many of these methods are designed for undirected networks only and based on domain-specific heuristics. Here we developed a new link prediction method based on deep generative models, which does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities. When applied to various real-world networks from different domains, our method shows overall superior performance against existing methods.Graphical AbstractDisplay OmittedHighlights•A novel link prediction method based on deep generative models is developed•This method works for general undirected or directed complex networks•Leveraging structural patterns at different scales, this method outperforms othersComplex Systems; Network Modeling; Network Topology