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The presence of a large number of bots on social media has adverse effects.The graph neural network (GNN) can effectively leverage the socialrelationships 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 onlow-frequency information and seldom consider high-frequency information, whichlimits the representation ability of the model. To address this issue, thispaper proposes a Multi-scale with Signed-attention Graph Filter for social botdetection called MSGS. MSGS could effectively utilize both high andlow-frequency information in the social graph. Specifically, MSGS utilizes amulti-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 producethe final result. We analyze the frequency response and demonstrate that MSGSis a more flexible and expressive adaptive graph filter. MSGS can effectivelyutilize high-frequency information to alleviate the over-smoothing problem ofdeep GNNs. Experimental results on real-world datasets demonstrate that ourmethod achieves better performance compared with several state-of-the-artsocial bot detection methods.

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