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.

翻译：社交媒体上大量机器人的存在带来了不利影响。图神经网络（GNN）能够有效利用用户之间的社交关系，在机器人检测中取得优异成果。近年来，越来越多的基于GNN的方法被提出用于机器人检测。然而，现有基于GNN的机器人检测方法仅关注低频信息，很少考虑高频信息，这限制了模型的表示能力。为解决这一问题，本文提出了一种面向社交机器人检测的带符号注意力多尺度图滤波器，称为MSGS。MSGS能有效利用社交图中的高频和低频信息。具体而言，MSGS采用多尺度结构生成不同尺度的表示向量，随后通过带符号注意力机制对这些表示进行组合。最后，聚合后的多尺度表示经由多层感知机（MLP）产生最终结果。我们分析了频率响应，并证明MSGS是一种更灵活、更具表达能力的自适应图滤波器。MSGS能有效利用高频信息缓解深度GNN的过度平滑问题。在真实数据集上的实验结果表明，与多种最先进的社交机器人检测方法相比，我们的方法取得了更优的性能。