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Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
Kiarash Zahirnia · Oliver Schulte · Parmis Naddaf · Ke Li

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #342

Generative models for graph data are an important research topic in machine learning. Graph data comprise two levels that are typically analyzed separately: node-level properties such as the existence of a link between a pair of nodes, and global aggregate graph-level statistics, such as motif counts.This paper proposes a new multi-level framework that jointly models node-level properties and graph-level statistics, as mutually reinforcing sources of information. We introduce a new micro-macro training objective for graph generation that combines node-level and graph-level losses. We utilize the micro-macro objective to improve graph generation with a GraphVAE, a well-established model based on graph-level latent variables, that provides fast training and generation time for medium-sized graphs. Our experiments show that adding micro-macro modeling to the GraphVAE model improves graph quality scores up to 2 orders of magnitude on five benchmark datasets, while maintaining the GraphVAE generation speed advantage.

Author Information

Kiarash Zahirnia (Simon Fraser University)
Oliver Schulte (Simon Fraser University)
Oliver Schulte

Bio: Oliver Schulte is a Professor in the School of Computing Science at Simon Fraser University, Vancouver, Canada. He received his Ph.D. from Carnegie Mellon University in 1997. His current research focuses on machine learning for structured, relational, and event data. He has published sports analytics papers in leading AI and machine learning venues, and co-organized two hockey analytics conferences. The last two years he has worked with Sportlogiq, a leading hockey data provider. While he has won some nice awards, his biggest claim to fame may be a draw against chess world champion Gary Kasparov.

Parmis Naddaf (Simon Fraser University)
Ke Li (Simon Fraser University)

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