Learning from Feature and Global Topologies: Adaptive Multi-View Parallel Graph Contrastive Learning

Yumeng Song, Xiaohua Li, Fangfang Li and Ge Yu ()
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Yumeng Song: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Xiaohua Li: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Fangfang Li: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Ge Yu: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China

Mathematics, 2024, vol. 12, issue 14, 1-26

Abstract: To address the limitations of existing graph contrastive learning methods, which fail to adaptively integrate feature and topological information and struggle to efficiently capture multi-hop information, we propose an adaptive multi-view parallel graph contrastive learning framework (AMPGCL). It is an unsupervised graph representation learning method designed to generate task-agnostic node embeddings. AMPGCL constructs and encodes feature and topological views to mine feature and global topological information. To encode global topological information, we introduce an H-Transformer to decouple multi-hop neighbor aggregations, capturing global topology from node subgraphs. AMPGCL learns embedding consistency among feature, topology, and original graph encodings through a multi-view contrastive loss, generating semantically rich embeddings while avoiding information redundancy. Experiments on nine real datasets demonstrate that AMPGCL consistently outperforms thirteen state-of-the-art graph representation learning models in classification accuracy, whether in homophilous or non-homophilous graphs.

Keywords: contrastive learning; parallel deep learning; graph neural network; graph representation learning; self-supervised learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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