Multi-scale graph classification with shared graph neural network

Zhou, Peng; Wu, Zongqian; Wen, Guoqiu; Tang, Kun; Ma, Junbo

doi:10.1007/s11280-022-01070-x

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…In contrast to traditional neural networks, GNNs are characterized by their unique message-passing approach, allowing iterative propagation and aggregation of information between nodes, thus enriching and enhancing the representation of nodes. Across multiple downstream tasks, including node classification ( Shi et al, 2021 ; Lin et al, 2023 ; Zou et al, 2023 ), link prediction ( Liu et al, 2022 ; Liu X. et al, 2023 ), and graph classification ( Zhou et al, 2023 ), GNNs have exhibited outstanding performance.…”

Section: Introductionmentioning

confidence: 99%

TP-GCL: graph contrastive learning from the tensor perspective

Li,

Meng,

et al. 2024

Front. Neurorobot.

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Graph Neural Networks (GNNs) have demonstrated significant potential as powerful tools for handling graph data in various fields. However, traditional GNNs often encounter limitations in information capture and generalization when dealing with complex and high-order graph structures. Concurrently, the sparse labeling phenomenon in graph data poses challenges in practical applications. To address these issues, we propose a novel graph contrastive learning method, TP-GCL, based on a tensor perspective. The objective is to overcome the limitations of traditional GNNs in modeling complex structures and addressing the issue of sparse labels. Firstly, we transform ordinary graphs into hypergraphs through clique expansion and employ high-order adjacency tensors to represent hypergraphs, aiming to comprehensively capture their complex structural information. Secondly, we introduce a contrastive learning framework, using the original graph as the anchor, to further explore the differences and similarities between the anchor graph and the tensorized hypergraph. This process effectively extracts crucial structural features from graph data. Experimental results demonstrate that TP-GCL achieves significant performance improvements compared to baseline methods across multiple public datasets, particularly showcasing enhanced generalization capabilities and effectiveness in handling complex graph structures and sparse labeled data.

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