Muti-scale Graph Neural Network with Signed-attention for Social Bot
  Detection: A Frequency Perspective
Shuhao Shi Kai Qiao Zhengyan Wang Jie Yang Baojie Song Jian Chen Bin Yan
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.