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Non-local Graph Convolutional Network

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Abstract

Graph convolutional network (GCN) has led to state-of-the-art performance for structured data. The superior performance would be partly due to the convolutional operations that operate over local neighborhoods. However, the distant long-range dependencies in data are still challenging to capture since it requires deep stacks of convolutional operations. Moreover, missing links in structured data might further hurt the performance. This paper introduces non-locality augmented graph convolution blocks into GCN to capture long-range or even disconnected dependencies. Specifically, we propose a dictionary-based non-locality encoding approach in which the non-local information is encoded by both graph convolution and dictionary-based implicit convolution. Unlike previous non-local approaches, our non-local block does not rely on the exhaustive computation of the relationship of data pairs. Thus, it is suitable for GCN, which typically models a large number of data samples. What’s more, the proposed non-local blocks could be embedded into arbitrarily GCN architectures. We demonstrate the efficacy of our non-local block on four benchmark datasets.

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Data Availability

The datasets analyzed during the current study are publically available. Cora, CiteSeer, and PubMed: http://linqs.umiacs.umd.edu/projects//projects/lbc/. Ogbn-arxiv: https://ogb.stanford.edu/docs/nodeprop/.

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Acknowledgements

The research was supported by the National Natural Science Foundation of China under Grant No. 62072468, the Natural Science Foundation of Shandong Province, China (Grant No. ZR2019MF073), the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China) (Grant No. 20CX05001A), the Major Scientific and Technological Projects of CNPC (No. ZD2019-183-008) and the Creative Research Team of Young Scholars at Universities in Shandong Province (No. 2019KJN019), the Major Basic Research Projects in Shandong Province (Grant No. ZR2023ZD32), the National Natural Science Foundation of China (Grant No. 62372468), the State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development(Grant No. 33550000-22-ZC0613-0243), the Shandong Natural Science Foundation (Grant No. ZR2023MF008), and the Qingdao Natural Science Foundation (Grant No. 23-2-1-161-zyyd-jch).

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Author notes

  1. Chunyu Du and Shuai Shao contributed equally to this work.

Authors and Affiliations

  1. College of Control Science and Engineering, China University of Petroleum (East China), West Changjiang Road, Huangdao District, Qingdao, 266580, Shandong, People’s Republic of China

    Chunyu Du, Jun Tang, Xinjing Song, Weifeng Liu, Baodi Liu & Yanjiang Wang

  2. Zhejiang Lab Nanhu Headquarters, Kechuang Avenue, Yuhang District, Hangzhou, 311121, Zhejiang, People’s Republic of China

    Shuai Shao

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  1. Chunyu Du
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Contributions

CD contributed to methodology, design experimental program, and writing—original draft; SS was involved in data preprocessing and writing—original draft and provided software; JT and XS contributed to formal analysis and investigation; WL was involved in resources and supervision; and YW and BL were involved in conceptualization, funding acquisition, resources, supervision, and writing—review and editing.

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Correspondence to Baodi Liu or Yanjiang Wang.

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Du, C., Shao, S., Tang, J. et al. Non-local Graph Convolutional Network. Circuits Syst Signal Process 43, 2095–2114 (2024). https://doi.org/10.1007/s00034-023-02563-4

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