Deep Ensembles for Graphs with Higher-order Dependencies

• URL: http://arxiv.org/abs/2205.13988v1
• Date: Fri, 27 May 2022 14:01:08 GMT
• Title: Deep Ensembles for Graphs with Higher-order Dependencies
• Authors: Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla
• Abstract summary: Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks. We show that the tendency of traditional graph representations to underfit each node's neighborhood causes existing GNNs to generalize poorly. We propose a novel Deep Graph Ensemble (DGE) which captures neighborhood variance by training an ensemble of GNNs on different neighborhood subspaces of the same node.
• Score: 13.164412455321907
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximation of the true neighborhood structure. In the presence of higher-order sequential dependencies, we show that the tendency of traditional graph representations to underfit each node's neighborhood causes existing GNNs to generalize poorly. To address this, we propose a novel Deep Graph Ensemble (DGE), which captures neighborhood variance by training an ensemble of GNNs on different neighborhood subspaces of the same node within a higher-order network structure. We show that DGE consistently outperforms existing GNNs on semisupervised and supervised tasks on four real-world data sets with known higher-order dependencies, even under a similar parameter budget. We demonstrate that learning diverse and accurate base classifiers is central to DGE's success, and discuss the implications of these findings for future work on GNNs.

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