Heterogeneous graph contrastive learning with metapath-based augmentations

Chen, X., Wang, Y., Fang, J., Meng, Z. and Liang, S. (2024) Heterogeneous graph contrastive learning with metapath-based augmentations. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(1), pp. 1003-1014. (doi: 10.1109/TETCI.2023.3322341)

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Heterogeneous graph contrastive learning is an effective method to learn discriminative representations of nodes in heterogeneous graph when the labels are absent. To utilize metapath in contrastive learning process, previous methods always construct multiple metapath-based graphs from the original graph with metapaths, then perform data augmentation and contrastive learning on each graph respectively. However, this paradigm suffers from three defects: 1) It does not consider the augmentation scheme on the whole metapath-based graph set, which hinders them from fully leveraging the information of metapath-based graphs to achieve better performance. 2) The final node embeddings are not optimized from the contrastive objective directly, so they are not guaranteed to be distinctive enough. It leads to suboptimal performance on downstream tasks. 3) Its computational complexity for contrastive objective is high. To tackle these defects, we propose a H eterogeneous G raph C ontrastive learning model with M etapath-based A ugmentations ( HGCMA ), which is designed for downstream tasks with a small amount of labeled data. To address the first defect, both semantic-level and node-level augmentation schemes are proposed in our HGCMA for augmentation, where a metapath-based graph and a certain ratio of edges in each metapath-based graph are randomly masked, respectively. To address the second and third defects, we utilize a two-stage attention aggregation graph encoder to output final node embedding and optimize them with contrastive objective directly. Extensive experiments on three public datasets validate the effectiveness of HGCMA when compared with state-of-the-art methods.

Item Type:Articles
Additional Information:This work was supported in part by the National Natural Science Foundation of China under Grant 61906219 and in part by MBZUAI.
Glasgow Author(s) Enlighten ID:Fang, Jinyuan and Meng, Dr Zaiqiao
Authors: Chen, X., Wang, Y., Fang, J., Meng, Z., and Liang, S.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Transactions on Emerging Topics in Computational Intelligence
ISSN (Online):2471-285X
Published Online:25 October 2023
Copyright Holders:Copyright © 2023 IEEE
First Published:First published in IEEE Transactions on Emerging Topics in Computational Intelligence 2023
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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