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

• URL: http://arxiv.org/abs/2210.16844v1
• Date: Sun, 30 Oct 2022 13:45:21 GMT
• Title: Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
• Authors: Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf, Ke Li
• Abstract summary: This paper proposes a new multi-level framework that jointly models node-level properties and graph-level statistics. We introduce a new micro-macro training objective for graph generation that combines node-level and graph-level losses. 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.
• Score: 16.302222204710276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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