Higher-order Clustering and Pooling for Graph Neural Networks

• URL: http://arxiv.org/abs/2209.03473v1
• Date: Fri, 2 Sep 2022 09:17:10 GMT
• Title: Higher-order Clustering and Pooling for Graph Neural Networks
• Authors: Alexandre Duval, Fragkiskos Malliaros
• Abstract summary: Graph Neural Networks achieve state-of-the-art performance on a plethora of graph classification tasks. HoscPool is a clustering-based graph pooling operator that captures higher-order information hierarchically. We evaluate HoscPool on graph classification tasks and its clustering component on graphs with ground-truth community structure.
• Score: 77.47617360812023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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