Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders

Kiarash Zahirnia · Oliver Schulte · Parmis Naddaf · Ke Li

Hall J #342

Keywords: [ Node-Level Properties ] [ Graph-Level Properties. Graph Variational Auto-Encoder ] [ Graph Generative Model ]

[ Abstract ]
[ Paper [ Slides [ Poster [ OpenReview
Tue 29 Nov 9 a.m. PST — 11 a.m. PST
Spotlight presentation: Lightning Talks 1B-2
Tue 6 Dec 9:30 a.m. PST — 9:45 a.m. PST


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.

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