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Structure-adaptive graph neural network with temporal representation and residual connections

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Abstract

Graph learning methods have boosted brain analysis for user healthcare, disease detection, and behavioral modeling. Spatially separated brain regions are functionally connected with different weights, enabling the classification of brain networks from the perspective of graph learning. However, existing methods based on graph neural networks mainly rely on the calculation of node feature correlation and manual threshold selection to obtain the graph structure, which disregards the temporal features of nodes and the latent information in the implicit graph structure. To address this problem, we propose a structure adaptive graph neural network with temporal representation and residual connections (TR-SAGNN) for brain network classification. First, we design a temporal attention learning module to learn the temporal features of the node itself. We design an end-to-end adaptive graph structure learning module based on the product-moment self-attention mechanism, which avoids manual threshold selection and obtains a more accurate graph structure. Second, we design a graph representation learning module based on a residual connection strategy to avoid the problem of insufficient propagation of node features. Last, we design a loss function to consider both the graph classification task and node classification task, which makes the model obtain better graph representation learning ability under the supervision of the node classification label. We conduct extensive experiments on the ANDI dataset. The results show that our model has better end-to-end adaptive graph construction capability as well as feature learning and classification performance.

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Acknowledgements

The work is supported by National Key R &D Program of China (Grant No. 2022YFB3304300), the National Natural Science Foundation of China (Grant No. 62072087, 61972077), LiaoNing Revitalization Talents Program (Grant No. XLYC2007079).

Funding

The authors would like to acknowledge the support provided by National Key R &D Program of China (Grant No. 2022YFB3304300), the National Natural Science Foundation of China (Grant No. 62072087, 61972077), LiaoNing Revitalization Talents Program (Grant No. XLYC2007079).

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Authors and Affiliations

  1. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, 110819, Shenyang, China

    Xin Bi & Zhixun Liu

  2. Key Laboratory of Liaoning Province on Deep Engineering and Intelligent Technology, Northeastern University, 110819, Shenyang, China

    Xin Bi & Zhixun Liu

  3. School of Computer Science and Engineering, Northeastern University, 110819, Shenyang, China

    Xin Bi, Qingling Jiang, Haojie Nie & Yongjiao Sun

  4. College of Software, Northeastern University, 110819, Shenyang, China

    Xin Yao & Xiangguo Zhao

  5. Thinvent Digital Technology Co., Ltd., 330000, Nanchang, Jiangxi, China

    George Y. Yuan

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  1. Xin Bi
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  2. Qingling Jiang
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  4. Xin Yao
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  7. Xiangguo Zhao
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Contributions

Xin Bi provided the conceptual design of the study, Qingling Jiang and Zhixun Liu wrote the main manuscript text and completed the experiments, Xin Yao and Haojie Nie prepared all the figures, Xiangguo Zhao and Yongjiao Sun provided supervision for the paper. All the authors reviewed the manuscript.

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Correspondence to Xin Bi.

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Xin Bi contributed equally to this work

This article belongs to the Topical Collection: Special Issue on Fairness-driven User Behavioral Modelling and Analysis for Online RecommendationGuest editors: Jianxin Li, Guandong Xu, Xiang Ren, and Qing Li.

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Bi, X., Jiang, Q., Liu, Z. et al. Structure-adaptive graph neural network with temporal representation and residual connections. World Wide Web 26, 3389–3408 (2023). https://doi.org/10.1007/s11280-023-01179-7

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  • DOI: https://doi.org/10.1007/s11280-023-01179-7

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