Graph neural network based node embedding enhancement model for node classification

被引：1

作者：

Zeng J.-X. ^{[1
]}

Wang P.-H. ^{[1
]}

Ding Y.-D. ^{[1
]}

Lan L. ^{[1
]}

Cai L.-X. ^{[1
]}

Guan X.-H. ^{[1
]}

机构：

[1] Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2023年 / 57卷 / 02期

关键词：

deep learning; graph neural network; neural network; node classification; supervised node classification;

D O I：

10.3785/j.issn.1008-973X.2023.02.001

中图分类号：

学科分类号：

摘要：

In reality, the structure of most graphs could be noisy, i.e., including some noisy edges or ignoring some edges that exist between nodes in practice. To solve these challenges, a novel differentiable similarity module (DSM), which boosted node representations by digging implict association between nodes to improve the accuracy of node classification, was presented. Basic representation of each target node was learnt by DSM using an ordinary graph neural network (GNN), similar node sets were selected in terms of node representation similarity and the basic representation of the similar nodes was integrated to boost the target node’s representation. Mathematically, DSM is differentiable, so it is possible to combine DSM as plug-in with arbitrary GNNs and train them in an end-to-end fashion. DSM enables to exploit the implicit edges between nodes and make the learned representations more robust and discriminative. Experiments were conducted on several public node classification datasets. Results demonstrated that with GNNs equipped with DSM, the classification accuracy can be significantly improved, for example, GAT-DSM outperformed GAT by significant margins of 2.9% on Cora and 3.5% on Citeseer. © 2023 Zhejiang University. All rights reserved.

引用

页码：219 / 225

页数：6

共 25 条

[1] WU Z, PAN S, CHEN F, Et al., A comprehensive survey on graph neural networks [J], IEEE Transactions on Neural Networks and Learning Systems, 32, 1, pp. 4-24, (2019)
[2] YAO L, MAO C, LUO Y., Graph convolutional networks for text classification [C], Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 7370-7377, (2019)
[3] ZHANG Z, CUI P, ZHU W., Deep learning on graphs: a survey [J], IEEE Transactions on Knowledge and Data Engineering, 34, 1, pp. 249-270, (2022)
[4] AKOGLU L, TONG H, KOUTRA D., Graph based anomaly detection and description: a survey [J], Data Mining and Knowledge Discovery, 29, 3, pp. 626-688, (2015)
[5] WANG P, XU B, XU J, Et al., Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification [J], Neurocomputing, 174, pp. 806-814, (2016)
[6] SHCHUR O, MUMME M, BOJCHEVSKI A, Et al., Pitfalls of graph neural network evaluation
[7] ZHU D, ZHANG Z, CUI P, Et al., Robust graph convolutional networks against adversarial attacks, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2019)
[8] HENAFF M, BRUNA J, LECUN Y., Deep convolutional networks on graph-structured data
[9] ZHANG Y, PAL S, COATES M J, Et al., Bayesian graph convolutional neural networks for semi-supervised classification [C], Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 5829-5836, (2019)
[10] HAMILTON W, YING Z, LESKOVEC J., Inductive Representation learning on large graphs [C], Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1024-1034, (2017)

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