Link Prediction through Deep Generative Model

Xu-Wen Wang; Yize Chen; Yang-Yu Liu

doi:10.1016/j.isci.2020.101626

Link Prediction through Deep Generative Model

iScience. 2020 Sep 28;23(10):101626. doi: 10.1016/j.isci.2020.101626. eCollection 2020 Oct 23.

Authors

Xu-Wen Wang¹, Yize Chen², Yang-Yu Liu¹

Affiliations

¹ Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
² Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA.

Abstract

Inferring 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.

Keywords: Complex Systems; Network Modeling; Network Topology.

Abstract

Grants and funding