Scalable temporal latent space inference for link prediction in dynamic social networks

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

Research output: Contribution to journalArticlepeer-review

198 Scopus citations


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 link prediction in terms of both scalability and predictive power.

Original languageEnglish (US)
Article number7511675
Pages (from-to)2765-2777
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number10
StatePublished - Oct 2016


  • Latent space model
  • link prediction
  • non-negative matrix factorization
  • social network analysis

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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