Friend Link Prediction Method Based on Heterogeneous Multigraph and Hierarchical Attention.
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| Title: | Friend Link Prediction Method Based on Heterogeneous Multigraph and Hierarchical Attention. |
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| Authors: | Liu, Aoxue1 (AUTHOR), Li, Boyu1 (AUTHOR), Wang, Yong1 (AUTHOR) yongwang@cug.edu.cn, Yang, Ziteng1 (AUTHOR) |
| Source: | Applied Artificial Intelligence. Dec2025, Vol. 39 Issue 1, p1-37. 37p. |
| Subjects: | Graph neural networks, Multigraph, Geospatial data, Attention, Social network analysis, Geotagging |
| Abstract: | With the rapid growth of location-based social network (LBSN), rich data comprising social behaviors and location information among users has emerged. Predicting potential friendships accurately from abundant information has become a pivotal research area. While graph neural network (GNN) have shown significant promise in prediction, existing approaches often fail to fully exploit the heterogeneous data characteristics in LBSN. Key challenges include inadequate modeling of the intricate relationships between users and points of interest (POI), overlooking the significance of spatial-temporal information in user trajectories, and underutilizing rich edge features. To address these challenges, we design a novel GRU-enhanced Heterogeneous Multigraph Attention Network (GEHMAN), which is a GNN model enhanced by GRU. We construct a heterogeneous multigraph to comprehensively capture user-POI relationships. We then employ a skip-gram model to embed POI nodes from user sub-trajectories and use RNN with GRU units to embed user nodes. GEHMAN utilize hierarchical attention mechanism to consolidate node information by aggregating diverse types of neighboring nodes and connecting edges. Experiments on six real city datasets show that compared with the best performance of six benchmark methods including LBSN2vec++, Metapath2vec and HAN, the average improvement percentages of GEHMAN in AUC, AP, and Top@K are 2.225%, 1.948%, and 6.353%, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189934051 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Friend Link Prediction Method Based on Heterogeneous Multigraph and Hierarchical Attention. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Aoxue%22">Liu, Aoxue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Boyu%22">Li, Boyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yong%22">Wang, Yong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yongwang@cug.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Ziteng%22">Yang, Ziteng</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Artificial+Intelligence%22">Applied Artificial Intelligence</searchLink>. Dec2025, Vol. 39 Issue 1, p1-37. 37p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Multigraph%22">Multigraph</searchLink><br /><searchLink fieldCode="DE" term="%22Geospatial+data%22">Geospatial data</searchLink><br /><searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink><br /><searchLink fieldCode="DE" term="%22Social+network+analysis%22">Social network analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Geotagging%22">Geotagging</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the rapid growth of location-based social network (LBSN), rich data comprising social behaviors and location information among users has emerged. Predicting potential friendships accurately from abundant information has become a pivotal research area. While graph neural network (GNN) have shown significant promise in prediction, existing approaches often fail to fully exploit the heterogeneous data characteristics in LBSN. Key challenges include inadequate modeling of the intricate relationships between users and points of interest (POI), overlooking the significance of spatial-temporal information in user trajectories, and underutilizing rich edge features. To address these challenges, we design a novel GRU-enhanced Heterogeneous Multigraph Attention Network (GEHMAN), which is a GNN model enhanced by GRU. We construct a heterogeneous multigraph to comprehensively capture user-POI relationships. We then employ a skip-gram model to embed POI nodes from user sub-trajectories and use RNN with GRU units to embed user nodes. GEHMAN utilize hierarchical attention mechanism to consolidate node information by aggregating diverse types of neighboring nodes and connecting edges. Experiments on six real city datasets show that compared with the best performance of six benchmark methods including LBSN2vec++, Metapath2vec and HAN, the average improvement percentages of GEHMAN in AUC, AP, and Top@K are 2.225%, 1.948%, and 6.353%, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/08839514.2024.2427545 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 37 StartPage: 1 Subjects: – SubjectFull: Graph neural networks Type: general – SubjectFull: Multigraph Type: general – SubjectFull: Geospatial data Type: general – SubjectFull: Attention Type: general – SubjectFull: Social network analysis Type: general – SubjectFull: Geotagging Type: general Titles: – TitleFull: Friend Link Prediction Method Based on Heterogeneous Multigraph and Hierarchical Attention. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Aoxue – PersonEntity: Name: NameFull: Li, Boyu – PersonEntity: Name: NameFull: Wang, Yong – PersonEntity: Name: NameFull: Yang, Ziteng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08839514 Numbering: – Type: volume Value: 39 – Type: issue Value: 1 Titles: – TitleFull: Applied Artificial Intelligence Type: main |
| ResultId | 1 |
