Dynamic Labeling for Unlabeled Graph Neural Networks

• URL: http://arxiv.org/abs/2102.11485v1
• Date: Tue, 23 Feb 2021 04:30:35 GMT
• Title: Dynamic Labeling for Unlabeled Graph Neural Networks
• Authors: Zeyu Sun, Wenjie Zhang, Lili Mou, Qihao Zhu, Yingfei Xiong, Lu Zhang
• Abstract summary: Graph neural networks (GNNs) rely on node embeddings to represent a node as a vector by its identity, type, or content. Existing GNNs either assign random labels to nodes or assign one embedding to all nodes, which fails to distinguish one node from another. In this paper, we analyze the limitation of existing approaches in two types of classification tasks, graph classification and node classification.
• Score: 34.65037955481084
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 unlabeled 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 distinguish one node from another). In this paper, we analyze the limitation of existing approaches in two types of classification tasks, graph classification and node classification. Inspired by our analysis, we propose two techniques, Dynamic Labeling and Preferential Dynamic Labeling, that satisfy desired properties statistically or asymptotically for each type of the task. Experimental results show that we achieve high performance in various graph-related tasks.

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