Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective

• URL: http://arxiv.org/abs/2307.01968v1
• Date: Wed, 5 Jul 2023 00:40:19 GMT
• Title: Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective
• Authors: Shuhao Shi, Kai Qiao, Zhengyan Wang, Jie Yang, Baojie Song, Jian Chen, Bin Yan
• Abstract summary: The presence of a large number of bots on social media has adverse effects. The graph neural network (GNN) can effectively leverage the social relationships between users and achieve excellent results in detecting bots. This paper proposes a Multi-scale with Signed-attention Graph Filter for social bot detection called MSGS.
• Score: 10.089319405788277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The presence of a large number of bots on social media has adverse effects. The graph neural network (GNN) can effectively leverage the social relationships between users and achieve excellent results in detecting bots. Recently, more and more GNN-based methods have been proposed for bot detection. However, the existing GNN-based bot detection methods only focus on low-frequency information and seldom consider high-frequency information, which limits the representation ability of the model. To address this issue, this paper proposes a Multi-scale with Signed-attention Graph Filter for social bot detection called MSGS. MSGS could effectively utilize both high and low-frequency information in the social graph. Specifically, MSGS utilizes a multi-scale structure to produce representation vectors at different scales. These representations are then combined using a signed-attention mechanism. Finally, multi-scale representations via MLP after polymerization to produce the final result. We analyze the frequency response and demonstrate that MSGS is a more flexible and expressive adaptive graph filter. MSGS can effectively utilize high-frequency information to alleviate the over-smoothing problem of deep GNNs. Experimental results on real-world datasets demonstrate that our method achieves better performance compared with several state-of-the-art social bot detection methods.

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