Unsupervised Graph Structure Learning Based on Optimal Graph Topology Modeling and Adaptive Data Augmentation

Dongdong An, Zongxu Pan, Qin Zhao, Wenyan Liu and Jing Liu ()
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Dongdong An: Shanghai Engineering Research Center of Intelligent Education and Bigdata, Shanghai Normal University, Shanghai 200234, China
Zongxu Pan: Shanghai Engineering Research Center of Intelligent Education and Bigdata, Shanghai Normal University, Shanghai 200234, China
Qin Zhao: Shanghai Engineering Research Center of Intelligent Education and Bigdata, Shanghai Normal University, Shanghai 200234, China
Wenyan Liu: Ant Group, Hangzhou 310023, China
Jing Liu: Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, China

Mathematics, 2024, vol. 12, issue 13, 1-22

Abstract: Graph neural networks (GNNs) are effective for structured data analysis but face reduced learning accuracy due to noisy connections and the necessity for explicit graph structures and labels. This requirement constrains their usability in diverse graph-based applications. In order to address these issues, considerable research has been directed toward graph structure learning that aims to denoise graph structures concurrently and refine GNN parameters. However, existing graph structure learning approaches encounter several challenges, including dependence on label information, underperformance of learning algorithms, insufficient data augmentation methods, and limitations in performing downstream tasks. We propose Uogtag, an unsupervised graph structure learning framework to address these challenges. Uogtag optimizes graph topology through the selection of suitable graph learners for the input data and incorporates contrastive learning with adaptive data augmentation, enhancing the learning and applicability of graph structures for downstream tasks. Comprehensive experiments on various real-world datasets demonstrate Uogtag’s efficacy in managing noisy graphs and label scarcity.

Keywords: graph neural networks; unsupervised learning; graph structure learning; contrastive learning on graphs (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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