Effective Link Prediction with Topological and Temporal Information using Wavelet Neural Network Embedding. (23rd July 2020)

• Record Type: Journal Article
• Authors: Mo, Xian; Pang, Jun; Liu, Zhiming
• Is Part Of: Computer journal. Volume 64:Number 3(2021)
• Page Start: 325
• Page End: 336
• Publication Date: 2020-07-23
• Subjects: temporal networks -- link prediction -- network embedding -- topological and temporal features -- graph wavelet neural networks -- gated recurrent units; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxaa085
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Temporal networks are networks that edges evolve over time, hence link prediction in temporal networks aims at inferring new edges based on a sequence of network snapshots. In this paper, we propose a graph wavelet neural network (TT-GWNN) framework using topological and temporal features for link prediction in temporal networks. To capture topological and temporal features, we develope a second-order weighted random walk sampling algorithm. It combines network snapshots with both first-order and second-order weights into one weighted graph. Moreover, it incorporates a damping factor to assign greater weights to more recent snapshots. Next, we adopt graph wavelet neural networks to embed the vertices and use gated recurrent units for predicting new links. Extensive experiments demonstrate that TT-GWNN can effectively predict links on temporal networks.