Rumor Detection Based on Bi-directional Graph Attention Network

Bai, Chuanzheng; Wang, Huidong; Wang, Lu; Yao, Jinli

doi:10.1109/cac53003.2021.9728123

Cited by 1 publication

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bian et al Bian et al (2020) applied Graph Neural Network to rumor detection, proposed a bidirectional graph convolutional neural network model with enhanced root node information, which considered the importance of the content information of the source post in the spread of rumors. Later, Bai proposed to convert the Graph Convolutional Network in this model into a Graph Attention Network Bai, Wang, Wang, and Yao (2021), which can ignore the additional role of root node information and achieve better discrimination results.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Federated Graph Attention Network for Rumor Detection

Wang¹,

Bai²,

Yao³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

With the development of network technology, many social media are flourishing. Due to imperfect Internet regulation, the spread of false rumors has become a common problem on those social platforms. Social platforms can generate rumor data in their operation process, but existing rumor detection models are all constructed for a single social platform, which ignores the value of cross-platform rumor. This paper combines the federated learning paradigm with the bidirectional graph attention network rumor detection model and proposes the federated graph attention network(FedGAT) model for rumor detection. Taking the advantages of horizontal federated learning for cross-platform rumor detection, the security of each social platform's data information can be ensured. We construct both the server-side and client-side models as a bidirectional graph attention network rumor detection model in the federated learning paradigm framework. The local model on the client-side can train and verify the rumor data of the social platform in the iterative process, while the model on the server-side only plays the role of aggregating the characteristics of different social platform models and does not participate in the training of the model. Finally, we

show abstract

Section: Related Workmentioning

confidence: 99%

“…4. Bi-GAT Bai et al (2021): The rumor detection model is based on a bidirectional graph attention neural network, which assigns different weights to different nodes in the graph structure. 5.…”

Section: Comparative Analysis Of Rumor Detection Experimentsmentioning

confidence: 99%