Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/28749
Title: | HGNN-QSSA: Heterogeneous Graph Neural Networks With Quantitative Sampling and Structure-Aware Attention |
Authors: | Zhao, Q Miao, Y An, D Lian, J Li, M |
Keywords: | heterogeneous information network;community detection;quantitative sampling;structure-aware attention |
Issue Date: | 14-Feb-2024 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Zhao, Q. et al. (2024) 'HGNN-QSSA: Heterogeneous Graph Neural Networks With Quantitative Sampling and Structure-Aware Attention', IEEE Access, 12, pp. 25512 - 25524. doi: 10.1109/ACCESS.2024.3366231. |
Abstract: | Heterogeneous information networks provide abundant structural and semantic information. Two main strategies for leveraging this data include meta-path-based and meta-path-free methods. The effectiveness of the former heavily depends on the quality of manually defined meta-paths, which may lead to the instability of the model. However, the existing meta-path-free methods lack of neighbor screening during aggregating, and there is also an overemphasis on attribute information. To address these issues, we propose the Heterogeneous Graph Neural Network model by incorporating Quantitative Sampling and Structure-aware Attention. We introduce a Quantitative Sampling Module that calculates the similarity between neighbors of the target nodes and target nodes, enabling us to select the top k nodes with the strongest relevance to the target node based on this measure, and incorporate a Structure-aware Attention Module during the aggregation of neighbor information. This module combines both structural and attribute information to aggregate the neighbor information effectively. By implementing these improvements, our proposed model exhibits superior performance compared to several state-of-the-art methods on two real-world datasets. |
URI: | https://bura.brunel.ac.uk/handle/2438/28749 |
DOI: | https://doi.org/10.1109/ACCESS.2024.3366231 |
Other Identifiers: | ORCiD: Qin Zhao https://orcid.org/0000-0001-7579-2004 ORCiD: Yaru Miao https://orcid.org/0009-0007-4952-7806 ORCiD: Dongdong An https://orcid.org/0000-0002-1412-8182 ORCiD: Jie Lian https://orcid.org/0000-0002-2005-2022 ORCiD: Maozhen Li https://orcid.org/0000-0002-0820-5487 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 2024 The Authors. Published by Institute of Electrical and Electronics Engineers (IEEE). This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ | 1.44 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License