A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks.
We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction.
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.
Junming
J
Yin
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.
Qirong
Q
Ho
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.
Eric P
EP
Xing