From Graph Diffusion to Graph Classification

• URL: http://arxiv.org/abs/2411.17236v1
• Date: Tue, 26 Nov 2024 08:57:41 GMT
• Title: From Graph Diffusion to Graph Classification
• Authors: Jia Jun Cheng Xian, Sadegh Mahdavi, Renjie Liao, Oliver Schulte,
• Abstract summary: We show how graph diffusion models can be applied for graph classification. In experiments with a sampling-based inference method, our discriminative training objective achieves state-of-the-art graph classification accuracy.
• Score: 21.2763549550792
• License:
• Abstract: Generative models such as diffusion models have achieved remarkable success in state-of-the-art image and text tasks. Recently, score-based diffusion models have extended their success beyond image generation, showing competitive performance with discriminative methods in image {\em classification} tasks~\cite{zimmermann2021score}. However, their application to classification in the {\em graph} domain, which presents unique challenges such as complex topologies, remains underexplored. We show how graph diffusion models can be applied for graph classification. We find that to achieve competitive classification accuracy, score-based graph diffusion models should be trained with a novel training objective that is tailored to graph classification. In experiments with a sampling-based inference method, our discriminative training objective achieves state-of-the-art graph classification accuracy.

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