Generalized Equivariance and Preferential Labeling for GNN Node Classification

Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unattributed nodes widely exist in real-world applications (e. g. , anonymized social networks). Previous GNNs either assign random labels to nodes (which introduces artefacts to the GNN) or assign one embedding to all nodes (which fails to explicitly distinguish one node from another). Further, when these GNNs are applied to unattributed node classification problems, they have an undesired equivariance property, which are fundamentally unable to address the data with multiple possible outputs. In this paper, we analyze the limitation of existing approaches to node classification problems. Inspired by our analysis, we propose a generalized equivariance property and a Preferential Labeling technique that satisfies the desired property asymptotically. Experimental results show that we achieve high performance in several unattributed node classification tasks.

Authors

• Zeyu Sun University of Michigan, Ann Arbor
• Wenjie Zhang School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, China; Engineering Research Center of Intelligent Swarm Systems, Ministry of Education, Zhengzhou, 450001, China; National Supercomputing Center in Zhengzhou, Zhengzhou, 450001, China;
• Lili Mou Dept. Computing Science, Alberta Machine Intelligence Institute (Amii), University of Alberta Canada CIFAR AI Chair, Amii
• Qihao Zhu
• Yingfei Xiong
• Lu Zhang Dalian University of Technology

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