Abstract
Graph structure learning (GSL), which aims to optimize graph structure and learn suitable parameters of graph neural networks (GNNs) simultaneously, has shown great potential in boosting the performance of GNNs. As a branch of GSL, multi-view methods mainly learn an optimal graph structure (final view) from multiple information sources (basic views). However, basic views’ structural information is insufficient, existing methods ignore the fact that different views can complement each other. Moreover, existing methods obtain the final view through simple combination, fail to constrain the noise, which inevitably brings irrelevant information. To tackle these problems, we propose a Gated Bi-View GSL architecture, named GBV-GSL, which interacts two basic views through a selection gating mechanism, so as to “turn off” noise as well as supplement insufficient structures. Specifically, two basic views that focus on different knowledge are extracted from original graph as two inputs of the model. Furthermore, we propose a novel view interaction technique based on selection gating mechanism to remove redundant structural information and supplement insufficient topology while retaining their focused knowledge. Finally, we design a view attention fusion mechanism to adaptively fuse two interacted views to generate the final view. In numerical experiments involving both clean and attacked conditions, GBV-GSL shows significant improvements in the effectiveness and robustness of structure learning and node representation learning. Code is available at https://github.com/Simba9257/GBV-GSL.
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Wang, X., Yan, H. (2024). Gated Bi-View Graph Structure Learning. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14452. Springer, Singapore. https://doi.org/10.1007/978-981-99-8076-5_29
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DOI: https://doi.org/10.1007/978-981-99-8076-5_29
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