Tackling Oversmoothing of GNNs with Contrastive Learning

• URL: http://arxiv.org/abs/2110.13798v1
• Date: Tue, 26 Oct 2021 15:56:16 GMT
• Title: Tackling Oversmoothing of GNNs with Contrastive Learning
• Authors: Lecheng Zheng, Dongqi Fu, Jingrui He
• Abstract summary: Graph neural networks (GNNs) integrate the comprehensive relation of graph data and representation learning capability. Oversmoothing makes the final representations of nodes indiscriminative, thus deteriorating the node classification and link prediction performance. We propose the Topology-guided Graph Contrastive Layer, named TGCL, which is the first de-oversmoothing method maintaining all three mentioned metrics.
• Score: 35.88575306925201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks (GNNs) integrate the comprehensive relation of graph data and the representation learning capability of neural networks, which is one of the most popular deep learning methods and achieves state-of-the-art performance in many applications, such as natural language processing and computer vision. In real-world scenarios, increasing the depth (i.e., the number of layers) of GNNs is sometimes necessary to capture more latent knowledge of the input data to mitigate the uncertainty caused by missing values. However, involving more complex structures and more parameters will decrease the performance of GNN models. One reason called oversmoothing is recently introduced but the relevant research remains nascent. In general, oversmoothing makes the final representations of nodes indiscriminative, thus deteriorating the node classification and link prediction performance. In this paper, we first survey the current de-oversmoothing methods and propose three major metrics to evaluate a de-oversmoothing method, i.e., constant divergence indicator, easy-to-determine divergence indicator, and model-agnostic strategy. Then, we propose the Topology-guided Graph Contrastive Layer, named TGCL, which is the first de-oversmoothing method maintaining all three mentioned metrics. With the contrastive learning manner, we provide the theoretical analysis of the effectiveness of the proposed TGCL. Last but not least, we design extensive experiments to illustrate the empirical performance of TGCL comparing with state-of-the-art baselines.

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