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DC Field | Value | Language |
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dc.contributor.author | Zhao, Q | - |
dc.contributor.author | Miao, Y | - |
dc.contributor.author | An, D | - |
dc.contributor.author | Lian, J | - |
dc.contributor.author | Li, M | - |
dc.date.accessioned | 2024-04-11T14:15:51Z | - |
dc.date.available | 2024-04-11T14:15:51Z | - |
dc.date.issued | 2024-02-14 | - |
dc.identifier | ORCiD: Qin Zhao https://orcid.org/0000-0001-7579-2004 | - |
dc.identifier | ORCiD: Yaru Miao https://orcid.org/0009-0007-4952-7806 | - |
dc.identifier | ORCiD: Dongdong An https://orcid.org/0000-0002-1412-8182 | - |
dc.identifier | ORCiD: Jie Lian https://orcid.org/0000-0002-2005-2022 | - |
dc.identifier | ORCiD: Maozhen Li https://orcid.org/0000-0002-0820-5487 | - |
dc.identifier.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. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28749 | - |
dc.description.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. | en_US |
dc.description.sponsorship | 10.13039/501100012166-National Key Research and Development Program of China (Grant Number: 2022YFB4501704); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62302308, U2142206, 62372300 and 61702333); Shanghai Sailing Program (Grant Number: 21YF1432900); Shanghai Engineering Research Center of Intelligent Education and Big Data; Research Base of Online Education for Shanghai Middle and Primary Schools. | en_US |
dc.format.extent | 25512 - 25524 | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | 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/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | heterogeneous information network | en_US |
dc.subject | community detection | en_US |
dc.subject | quantitative sampling | en_US |
dc.subject | structure-aware attention | en_US |
dc.title | HGNN-QSSA: Heterogeneous Graph Neural Networks With Quantitative Sampling and Structure-Aware Attention | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/ACCESS.2024.3366231 | - |
dc.relation.isPartOf | IEEE Access | - |
pubs.publication-status | Published | - |
pubs.volume | 12 | - |
dc.identifier.eissn | 2169-3536 | - |
dc.rights.license | https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.en | - |
dc.rights.holder | The Authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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