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Conference Papers Year : 2022

Higher-order Clustering and Pooling for Graph Neural Networks


Graph Neural Networks achieve state-of-the-art performance on a plethora of graph classification tasks, especially due to pooling operators, which aggregate learned node embeddings hierarchically into a final graph representation. However, they are not only questioned by recent work showing on par performance with random pooling, but also ignore completely higher-order connectivity patterns. To tackle this issue, we propose HoscPool, a clustering-based graph pooling operator that captures higher-order information hierarchically, leading to richer graph representations. In fact, we learn a probabilistic cluster assignment matrix end-to-end by minimising relaxed formulations of motif spectral clustering in our objective function, and we then extend it to a pooling operator. We evaluate HoscPool on graph classification tasks and its clustering component on graphs with ground-truth community structure, achieving best performance. Lastly, we provide a deep empirical analysis of pooling operators' inner functioning.
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Dates and versions

hal-03910823 , version 1 (22-01-2024)



Alexandre Duval, Fragkiskos D. Malliaros. Higher-order Clustering and Pooling for Graph Neural Networks. CIKM 2022 - 31st ACM International Conference on Information & Knowledge Management, Oct 2022, Atlanta, Georgia, United States. pp.426-435, ⟨10.1145/3511808.3557353⟩. ⟨hal-03910823⟩
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