Triangle Completion Time Prediction Using Time-Conserving Embedding

Abstract

A triangle is an important building block of social networks, so the study of triangle formation in a network is critical for better understanding of the dynamics of such networks. Existing works in this area mainly focus on triangle counting, or generating synthetic networks by matching the prevalence of triangles in real-life networks. While these efforts increase our understanding of triangle’s role in a network, they have limited practical utility. In this work we undertake an interesting problem relating to triangle formation in a network, which is, to predict the time by which the third link of a triangle appears in a network. Since the third link completes a triangle, we name this task as Triangle Completion Time Prediction (TCTP). Solution to TCTP problem is valuable for real-life link recommendation in social/e-commerce networks, also it provides vital information for dynamic network analysis and community generation study. An efficient and robust framework (GraNiTE) is proposed for solving the TCTP problem. GraNiTE uses neural networks based approach for learning a representation vector of a triangle completing edge, which is a concatenation of two representation vectors: first one is learnt from graphlet based local topology around that edge and the second one is learnt from time-preserving embedding of the constituting vertices of that edge. A comparison of the proposed solution with several baseline methods shows that the mean absolute error (MAE) of the proposed method is at least one-forth of that of the best baseline method.