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Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification

Sun, Zhongtian; Harit, Anoushka; Cristea, Alexandra I.; Yu, Jialin; Shi, Lei; Al Moubayed, Noura

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Zhongtian Sun
PGR Student Doctor of Philosophy

Jialin Yu
Academic Visitor

Lei Shi


Graph neural networks (GNNs) have attracted extensive interest in text classification tasks due to their expected superior performance in representation learning. However, most existing studies adopted the same semi-supervised learning setting as the vanilla Graph Convolution Network (GCN), which requires a large amount of labelled data during training and thus is less robust when dealing with large-scale graph data with fewer labels. Additionally, graph structure information is normally captured by direct information aggregation via network schema and is highly dependent on correct adjacency information. Therefore, any missing adjacency knowledge may hinder the performance. Addressing these problems, this paper thus proposes a novel method to learn a graph structure, NC-HGAT, by expanding a state-of-the-art self-supervised heterogeneous graph neural network model (HGAT) with simple neighbour contrastive learning. The new NC-HGAT considers the graph structure information from heterogeneous graphs with multilayer perceptrons (MLPs) and delivers consistent results, despite the corrupted neighbouring connections. Extensive experiments have been implemented on four benchmark short-text datasets. The results demonstrate that our proposed model NC-HGAT significantly outperforms state-of-the-art methods on three datasets and achieves competitive performance on the remaining dataset.


Sun, Z., Harit, A., Cristea, A. I., Yu, J., Shi, L., & Al Moubayed, N. (2022). Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification. .

Conference Name 2022 International Joint Conference on Neural Networks (IJCNN)
Conference Location Padova, Italy
Start Date Jul 18, 2022
End Date Jul 23, 2022
Acceptance Date Apr 26, 2022
Online Publication Date Sep 30, 2022
Publication Date 2022
Deposit Date Aug 31, 2022
Publicly Available Date Sep 1, 2022
Series ISSN 2161-4393,2161-4407


Accepted Conference Proceeding (394 Kb)

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