Social Bots Detection Based on Multi-relationship Graph Attention Network
At present,social bots have gained extensive utilization across social platforms and the existence of social bots makes the public opinion environment on the network artificially manipulated.This not only compromises the integrity of a healthy and harmonious online atmosphere but also significantly disrupts people's regular online activities.Existing detection methods can be divided into feature-based,text-based,and graph-based methods.However,graph-based detection methods predominantly ignore the heterogeneous relationships,and cannot perform deep detection due to the transition smoothing phenomenon in graph neural networks.To solve the above problems,a social bots detection method based on a multi-relationship graph attention network is proposed.Firstly,we extract subgraphs with different relationships,then apply the attention mechanism to aggregate the nodes within the subgraph and conduct node representation learning across diverse relationships,resulting in the acquisition of node rep-resentations.Finally,we use channel attention to fuse the same node under different relationships to obtain node representation,while using the post-connection operation based on LSTM attention to allow nodes to adaptively select neighborhoods for aggre-gation,thereby alleviating the over-smoothing phenomenon.Experiments are conducted on three datasets:Cresci15,Twibot20,and MGTAB,and the experimental results show that,compared with the optimal values of the evaluation indicators of 11 models,the accuracy of the model is increased by 0.47％,1.19％and 0.38％,respectively,which demonstrates the effectiveness of the multi-relationship graph attention network for social bots detection.
万方数据
维普
