Event-Driven Hypergraph Convolutional Network Based Rumor Detection Method

Zeng Zhi; Zhao Shuqing; Liu Huan; Zhao Xiang; Luo Minnan

doi:10.7544/issn1000-1239.202440136

Journal of Computer Research and Development > 2024 > 61(8): 1982-1992. > DOI: 10.7544/issn1000-1239.202440136

Zeng Zhi, Zhao Shuqing, Liu Huan, Zhao Xiang, Luo Minnan. Event-Driven Hypergraph Convolutional Network Based Rumor Detection Method[J]. Journal of Computer Research and Development, 2024, 61(8): 1982-1992. DOI: 10.7544/issn1000-1239.202440136

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Event-Driven Hypergraph Convolutional Network Based Rumor Detection Method

1.
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049
2.
Laboratory for Big Data and Decision (National University of Defense Technology), Changsha 410003

Funds: This work was supported the National Key Research and Development Program of China (2022YFB3102600), the National Natural Science Foundation of China (62192781, 62272374, 62202367, 62250009, 62137002), the Natural Science Foundation of Shaanxi Province (2024JC-JCQN-62), the Project of China Knowledge Center for Engineering Science and Technology, the Project of Chinese academy of engineering “The Online and Offline Mixed Educational Service System for ‘The Belt and Road’ Training in MOOC China”, and the K. C. Wong Education Foundation.

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Author Bio:
Zeng Zhi: born in 1998. PhD candidate. His main research interests include graph learning and explainable deep learning

Zhao Shuqing: born in 2001. Master candidate. His main research interests include graph learning and explainable deep learning

Liu Huan: born in 1990. PhD, assistant professor. His main research interests include machine learning, and computer vision and public opinion analysis

Zhao Xiang: born in 1986. PhD, professor. His main research interests include graph data management and mining, and intelligent analytics

Luo Minnan: born in 1984. Professor, PhD supervisor. Her main research interests include machine learning, graph learning, and cross-media data mining
Received Date: March 14, 2024
Revised Date: May 19, 2024
Available Online: July 04, 2024

Graphical Abstract

Abstract

Abstract

Rumor detection with propagation chain remains an important topic in the research of social network analysis. Previous studies mostly oversimplify this task as detecting rumor in propagation chains of comments. However, real-world news posts usually share complex user and event interactions, which can provide potential detection clues. Therefore, we propose an event-driven HyperGraph convolutional network (EHGCN), which makes the first attempt to model news, user and event correlations into a unified hypergraph convolutional network for rumor detection. Specifically, it exploits egocentric network of users to construct homogeneous users circles for enhancing user-aware rumor verification. Moreover, EHGCN jointly leverages the main event and sub-event patterns at the intra-event level for rumor detection. The extensive experimental results show that EHGCN could improve the performance of rumor detection. Studies also confirm that EHGCN can detect rumors at an early stage by acquiring rich user circle and event information.
- rumor detection,
- event-driven,
- hypergraph,
- convolutional network,
- homogeneous users circles

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References (30)

References

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