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Poster
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks
Junming Yin · Qirong Ho · Eric Xing

Thu Dec 05 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor
in Poster Session »

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.

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Author Information

Junming Yin (Carnegie Mellon University)
Qirong Ho (Petuum, Inc.)
Eric Xing (Petuum Inc. / Carnegie Mellon University)

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