Abstract

We propose a temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. The model assumes that each user lies in an unobserved latent space, and interactions are more likely to occur between similar users in the latent space representation. In addition, the model allows each user to gradually move its position in the latent space as the network structure evolves over time. We present a global optimization algorithm to effectively infer the temporal latent space. Two alternative optimization algorithms with local and incremental updates are also proposed, allowing the model to scale to larger networks without compromising prediction accuracy. Empirically, we demonstrate that our model, when evaluated on a number of real-world dynamic networks, significantly outperforms existing approaches for temporal …

Date

July 13, 2016

Authors

Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, Aram Galstyan

Journal

IEEE Transactions on Knowledge and Data Engineering

Volume

28

Issue

10

Pages

2765-2777

Publisher

IEEE