  We propose a scalable 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 form 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, with a quadratic convergence rate.
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.
