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Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Kaixiong Zhou · Xiao Huang · Daochen Zha · Rui Chen · Li Li · Soo-Hyun Choi · Xia Hu

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @

Graph neural networks (GNNs) integrate deep architectures and topological structure modeling in an effective way. However, the performance of existing GNNs would decrease significantly when they stack many layers, because of the over-smoothing issue. Node embeddings tend to converge to similar vectors when GNNs keep recursively aggregating the representations of neighbors. To enable deep GNNs, several methods have been explored recently. But they are developed from either techniques in convolutional neural networks or heuristic strategies. There is no generalizable and theoretical principle to guide the design of deep GNNs. To this end, we analyze the bottleneck of deep GNNs by leveraging the Dirichlet energy of node embeddings, and propose a generalizable principle to guide the training of deep GNNs. Based on it, a novel deep GNN framework -- Energetic Graph Neural Networks (EGNN) is designed. It could provide lower and upper constraints in terms of Dirichlet energy at each layer to avoid over-smoothing. Experimental results demonstrate that EGNN achieves state-of-the-art performance by using deep layers.

Author Information

Kaixiong Zhou (Rice University)
Xiao Huang (The Hong Kong Polytechnic University)
Daochen Zha (Texas A&M University)
Rui Chen (Samsung Research America)
Li Li (Samsung)
Soo-Hyun Choi (Samsung Electronics)
Xia Hu (Texas A&M University)

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