Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

Liang Zhang; Jingqun Li; Bin Zhou; Yan Jia

doi:10.20944/preprints202011.0511.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 18 November 2020 / Approved: 19 November 2020 / Online: 19 November 2020 (12:43:51 CET)
Version 2 : Received: 21 December 2020 / Approved: 22 December 2020 / Online: 22 December 2020 (14:23:02 CET)

Identifying fake news on the media has been an important issue. This is especially true considering the wide spread of rumors on the popular social networks such as Twitter. Various kinds of techniques have been proposed to detect rumors. In this work, we study the application of graph neural networks for the task of rumor detection, and present a simplified new architecture to classify rumors. Numerical experiments show that the proposed simple network has comparable to or even better performance than state-of-the art graph convolutional networks, while having significantly reduced the computational complexity.

Rumor detection; Graph neural network; Artificial intelligence

Engineering, Automotive Engineering

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Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

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