Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization
This work addresses the problem of predicting links over time in dynamic networks for researchers and practitioners, offering incremental improvements in scalability and accuracy.
The authors tackled link prediction in dynamic social networks by proposing a scalable temporal latent space model that assumes users move in a latent space over time, and they demonstrated that it significantly outperforms existing approaches in scalability and predictive power on real-world networks.
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.