Structure Learning Via Meta-Hyperedge for Dynamic Rumor Detection

Sun, X; Yin, H; Liu, B; Meng, Q; Cao, J; Zhou, A; Chen, H

Structure Learning Via Meta-Hyperedge for Dynamic Rumor Detection

Sun, X Yin, H Liu, B Meng, Q Cao, J Zhou, A Chen, H

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2023, 35, (9), pp. 9128-9139
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Sun, X
dc.contributor.author	Yin, H
dc.contributor.author	Liu, B
dc.contributor.author	Meng, Q
dc.contributor.author	Cao, J
dc.contributor.author	Zhou, A
dc.contributor.author	Chen, H https://orcid.org/0000-0001-7963-8813
dc.date.accessioned	2024-03-22T03:14:07Z
dc.date.available	2024-03-22T03:14:07Z
dc.date.issued	2023-09-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2023, 35, (9), pp. 9128-9139
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/177012
dc.description.abstract	Online social networks have greatly facilitated our lives but have also propagated the spreading of rumours. Traditional works mostly find rumors from content, but content can be strategically manipulated to evade such detection, making these methods brittle. To improve the accuracy and robustness of rumor detection, we propose to integrate and exploit the content, propagation structure, and temporal relations because information in the networks always spreads dynamically with significant structures. In this paper, we propose a novel rumor detection framework in online temporal networks via structure learning. Specifically, to exploit the propagation structure, we propose a novel hyperedge walking strategy on a meta-hyperedge graph to learn the representations of sub-structures in the networks. Then a hyperedge expansion method is proposed to generate more global structural features. The expanded hyperedges are more hierarchical, making the learned structural embeddings more expressive. To make full use of content, we design a hypergraph learning model using hyperedge expansion to fuse node content with structural features and generate comprehensive representations for the entire graph. To exploit temporal relations, we design a masked temporal attention unit for learning the evolving patterns of the network. Extensive evaluations with six state-of-the-art baselines on two real-world datasets demonstrate the superiority of our solution.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2022.3221438
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	Structure Learning Via Meta-Hyperedge for Dynamic Rumor Detection
dc.type	Journal Article
utslib.citation.volume	35
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-22T03:14:06Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	9

Abstract:

Online social networks have greatly facilitated our lives but have also propagated the spreading of rumours. Traditional works mostly find rumors from content, but content can be strategically manipulated to evade such detection, making these methods brittle. To improve the accuracy and robustness of rumor detection, we propose to integrate and exploit the content, propagation structure, and temporal relations because information in the networks always spreads dynamically with significant structures. In this paper, we propose a novel rumor detection framework in online temporal networks via structure learning. Specifically, to exploit the propagation structure, we propose a novel hyperedge walking strategy on a meta-hyperedge graph to learn the representations of sub-structures in the networks. Then a hyperedge expansion method is proposed to generate more global structural features. The expanded hyperedges are more hierarchical, making the learned structural embeddings more expressive. To make full use of content, we design a hypergraph learning model using hyperedge expansion to fuse node content with structural features and generate comprehensive representations for the entire graph. To exploit temporal relations, we design a masked temporal attention unit for learning the evolving patterns of the network. Extensive evaluations with six state-of-the-art baselines on two real-world datasets demonstrate the superiority of our solution.

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http://hdl.handle.net/10453/177012