Feature Driven Graph Coarsening for Scaling Graph Representation Learning
Keywords: Graph Coarsening, Graph Neural Networks
Abstract: Graphical modelling for structured data analysis has gained prominence across numerous domains. A significant computational challenge lies in efficiently capturing complex relationships within large-scale graph structures. Graph coarsening, which reduces graph size by merging nodes and edges into supernodes and superedges, enhances scalability and is crucial for graph neural networks (GNNs). However, current methods either construct graphs from large-scale attribute data or assume a pre-existing graph before coarsening, limiting their applicability, especially in domains like healthcare and finance where graph structure is often unavailable. In this paper, we present a novel framework that directly learns a coarsened graph from attribute information, reducing computational complexity and enhancing robustness against adversarial attacks, which commonly target vulnerabilities in graph structures. By integrating label information, our framework also enables semi-supervised learning, leading to improved performance on downstream tasks. Extensive experiments show that our method outperforms state-of-the-art coarsening techniques in both accuracy and computational efficiency.
Supplementary Material: zip
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 12838
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