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  Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection

Zhu, J., Gao, C., Yin, Z., Li, X., Kurths, J. (2024): Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection. - In: Baeza-Yates, R., Bonchi, F. (Eds.), KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York : Association for Computing Machinery, 4652-4663.
https://doi.org/10.1145/3637528.3672024

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 Urheber:
Zhu, Junyou1, Autor
Gao, Chao1, Autor
Yin, Ze1, Autor
Li, Xianghua1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: The rise of social media has intensified fake news risks, prompting a growing focus on leveraging graph learning methods such as graph neural networks (GNNs) to understand post-spread patterns of news. However, existing methods often produce less robust and interpretable results as they assume that all information within the propagation graph is relevant to the news item, without adequately eliminating noise from engaged users. Furthermore, they inadequately capture intricate patterns inherent in long-sequence dependencies of news propagation due to their use of shallow GNNs aimed at avoiding the over-smoothing issue, consequently diminishing their overall accuracy. In this paper, we address these issues by proposing the Propagation Structure-aware Graph Transformer (PSGT). Specifically, to filter out noise from users within propagation graphs, PSGT first designs a noise-reduction self-attention mechanism based on the information bottleneck principle, aiming to minimize or completely remove the noise attention links among task-irrelevant users. Moreover, to capture multi-scale propagation structures while considering long-sequence features, we present a novel relational propagation graph as a position encoding for the graph Transformer, enabling the model to capture both propagation depth and distance relationships of users. Extensive experiments demonstrate the effectiveness, interpretability, and robustness of our PSGT.

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Sprache(n): eng - Englisch
 Datum: 2024-08-242024-08-24
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1145/3637528.3672024
MDB-ID: No data to archive
Model / method: Machine Learning
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
 Art des Abschluß: -

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Titel: KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Genre der Quelle: Buch
 Urheber:
Baeza-Yates, Ricardo 1, Herausgeber
Bonchi, Francesco 1, Herausgeber
Affiliations:
1 External Organizations, ou_persistent22            
Ort, Verlag, Ausgabe: New York : Association for Computing Machinery
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 4652 - 4663 Identifikator: ISBN: 979-8-4007-0490-1