基于多关系图注意力网络的社交机器人检测

doi:10.11896/jsjkx.231100161

Abstract

Abstract: 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 representations.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 aggregation,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.

Key words: Heterogeneous graph, Graph attention, Nodes representation learning, LSTM attention, Social bots

CLC Number:

TP391

MENG Lingjun, CHEN Hongchang, WANG Gengrun. Social Bots Detection Based on Multi-relationship Graph Attention Network[J].Computer Science, 2025, 52(1): 298-306.

References

[1]MARIA K,ILIAS D,ATHENA V.Bot-Detective:An explai-nable Twitter bot detection service with crowdsourcing functionalities[C]//12th International Conference on Management of Digital Ecosystems.New York:Association for Computing Machinery,2020:55-63.
[2]WU Y H,FANG Y Z,SHANG S K,et al.A novel framework for detecting social bots with deep neural networks and active learning[J].European Journal of Medicinal Chemistry:Chimie Therapeutique,2021,211(1):1-16.
[3]ABREU J,GONDIM J,RALHA C.Twitter Bot Detection with Reduced Feature Set[C]//2020 IEEE International Conference on Intelligence and Security Informatics(ISI).USA:Arlington,2020:1-6.
[4]HU F X,LUO W H.Social robot account detection based onmulti-dimensional dynamic feature verification[J].Journal of Foshan University,2023,41(1):23-34.
[5]SNEHA K,EMILIO F.Deep Neural Networks for Bot Detection[J].Information Sciences,2018,467(10):312-322.
[6]GUO Q,XIE H,LI Y,et al.Social Bots Detection via Fusing BERT and Graph Convolutional Networks[J].Symmetry,2022,14(1):1-30.
[7]HAYAWI K,MATHEW S,VENUGOPAL N,et al.DeeProBot:a hybrid deep neural network model for social bot detection based on user profile data[J].Social Network Analysis and Mi-ning,2022,12(1):1-19.
[8]WU J,YE X,MAN Y.BotTriNet:A Unified and Efficient Embedding for Social Bots Detection via Metric Learning[C]//2023 11th International Symposium on Digital Forensics and Security(ISDFS).Turkey:Istanbul,2023:1-6.
[9]WANG X,JI H Y,SHI C,et al.Heterogeneous Graph Attention Network[C]//The World Wide Web Conference(WWW’19).New York:Association for Computing Machinery,2019:2022-2032.
[10]LI Y,JI Y,LI S,et al.Relevance-aware anomalous users’ detection in social network via graph neural network[C]//2021 International Joint Conference on Neural Networks(IJCNN).New York:IEEE,2021:1-8.
[11]FENG S B,WAN H R,WANG N N.SATAR:A Self-supervised Approach to Twitter Account Representation Learning and its Application in Bot Detection[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management.New York:Association for Computing Machinery,2021:3808-3817.
[12]FENG S B,WAN H R,WANG N N.BotRGCN:Twitter bot detection with relational graph convolutional networks[C]//Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.New York:Association for Computing Machinery,2021:236-239.
[13]YANG Y G,YANG R Y,LI Y Y,et al.RoSGAS:Adaptive Social Bot Detection with Reinforced Self-supervised GNN Architecture Search[J].ACM Transactions on the Web,2023,17(3):1-31.
[14]XU K Y,ZHOU A M,CHEN A L,et al.Social bot detection based on active learning and relational graph convolutional neural networks[J].Journal of Sichuan University(Natural Science Edition),2023,60(5):121-129.
[15]CAI Z J,TAN Z X,LEI Z Y,et al.LMBot:Distilling GraphKnowledge into Language Model for Graph-less Deployment in Twitter Bot Detection[J].arXiv:2306.17408,2023.
[16]SIRUSSTARA J,ALEXANDER N,ALFARISY A,et al.Clickbait Headline Detection in Indonesian News Sites using Robustly Optimized BERT Pre-training Approach(RoBERTa)[C]//2022 3rd International Conference on Artificial Intelligence and Data Sciences(AiDAS).IPOH:Malaysia,2022:1-6.
[17]VASWANI A,SHAZEER N,PARMAR N,et al.Attention IsAll You Need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems(NIPS’17).San Diego:NIPS,2017:6000-6010.
[18]SCHLICHTKRULL M,KIPF T,BLOEM P,et al.Modeling relational data with graph convolutional networks[C]//European Semantic Web Conference.European:Springer,2018:593-607.
[19]HU J,SHEN L,ALBANIE S,et al.Squeeze-and-Excitation Networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.2017:7132-7141.
[20]LI Q M,HAN Z C,WU X M.Deeper Insights Into Graph Con-volutional Networks for Semi-Supervised Learning[C]//Thirty-Second AAAI Conference on Artificial Intelligence.New York:AAAI Press,2018:3538-3545.
[21]JIANG Y,ZHAO T,CHAI Y,et al.Bidirectional LSTM-CRF models for keyword extraction in Chinese sport news[J].MIPPR 2019:Pattern Recognition and Computer Vision,2020,2(1):11-17.
[22]STEFANO C,ROBERTO D,MARINELLA P,et al.Fame for sale:Efficient detection of fake Twitter followers[J].Decision Support Systems,2015,80(9):56-71.
[23]FENG S B,WAN H,WANG N,et al.TwiBot-20:A Comprehensive Twitter Bot Detection Bench-mark[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management,New York:ACM,2020:4485-4494.
[24]SHI S H,QIAO K,CHEN J.MGTAB:A Multi-RelationalGraph-Based Twitter Account Detection Benchmark[C]//IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE,2023:1-14.
[25]WEI Y L.Classification and regression trees[J].Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery,2011,1(1):3-7.
[26]BREIMAN L.Random forests[J].Machine Learning,2004,45:5-32.
[27]SHA A,WANG B,WU X,et al.Semi-Supervised Classification for Hyperspectral Images Using Edge-Conditioned Graph Con-volutional Networks[C]//IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2019).Japan:Yokohama,2019:2690-2693.
[28]PETAR V,GUILLEM C,ARANTXA C,et al.Graph attention networks[C]//International Conference on Learning Representations(ICLR).Washington:ICLR,2018.
[29]WILL H,YING Z T,JURE L.Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems(NIPS’17).USA:Curran Associates Inc.2017:1025-1035.
[30]HU Z H,DONG Y X,WANG K S,et al.Heterogeneous graph transformer[C]//Proceedings of The Web Conference 2020(WWW’20).USA:Association for Computing Machinery,2020:2704-2710.
[31]YE S,TAN Z,LEI Z,et al.Hofa:Twitter bot detection with homophily-oriented augmentation and frequency adaptive attention[J].arXiv:2306.12870,2023.
[32]SHI S H,QIAO K,YANG J,et al.RF-GNN:Random Forest boosted graph neural network for social bot detection[J].arXiv:2304.08239,2023.
[33]LV Q S,DING M,LIU Q,et al.Are we really making much progress?Revisiting,benchmarking and refining heterogeneous graph neural networks[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.New York:ACM,2021:1150-1160.
[34]LU H Y,LIU F,WANG Y B.Social Bot Detection for Dynamic Social Networks Based on Link Prediction[J].Journal of Information Engineering University,2024,25(3):285-291.

Related Articles 15

[1]	YAN Qiuyan, SUN Hao, SI Yuqing, YUAN Guan. Multimodality and Forgetting Mechanisms Model for Knowledge Tracing [J]. Computer Science, 2024, 51(7): 133-139.
[2]	MAO Xingjing, WEI Yong, YANG Yurui, JU Shenggen. KHGAS:Keywords Guided Heterogeneous Graph for Abstractive Summarization [J]. Computer Science, 2024, 51(7): 278-286.
[3]	PENG Bo, LI Yaodong, GONG Xianfu, LI Hao. Method for Entity Relation Extraction Based on Heterogeneous Graph Neural Networks and TextSemantic Enhancement [J]. Computer Science, 2024, 51(6A): 230700071-5.
[4]	HOU Lei, LIU Jinhuan, YU Xu, DU Junwei. Review of Graph Neural Networks [J]. Computer Science, 2024, 51(6): 282-298.
[5]	WANG Xiaolong, WANG Yanhui, ZHANG Shunxiang, WANG Caiqin, ZHOU Yuhao. Gender Discrimination Speech Detection Model Fusing Post Attributes [J]. Computer Science, 2024, 51(6): 338-345.
[6]	ZHANG Zebao, YU Hannan, WANG Yong, PAN Haiwei. Combining Syntactic Enhancement with Graph Attention Networks for Aspect-based Sentiment Classification [J]. Computer Science, 2024, 51(5): 200-207.
[7]	LIAO Jinzhi, ZHAO Hewei, LIAN Xiaotong, JI Wenliang, SHI Haiming, ZHAO Xiang. Contrastive Graph Learning for Cross-document Misinformation Detection [J]. Computer Science, 2024, 51(3): 14-19.
[8]	PAN Lei, LIU Xin, CHEN Junyi, CHENG Zhangtao, LIU Leyuan, ZHOU Fan. Event Prediction Based on Dynamic Graph with Local Data Augmentation [J]. Computer Science, 2024, 51(3): 118-127.
[9]	SUN Shounan, WANG Jingbin, WU Renfei, YOU Changkai, KE Xifan, HUANG Hao. TMGAT:Graph Attention Network with Type Matching Constraint [J]. Computer Science, 2024, 51(3): 235-243.
[10]	LIN Huang, LI Bicheng. Aspect-based Sentiment Analysis Based on BERT Model and Graph Attention Network [J]. Computer Science, 2024, 51(11A): 240400018-7.
[11]	SUN Pengzhao, BI Kejun, TANG Chao, LI Dongfen, YING Shi, WANG Ruijin. Risk Assessment Model for Industrial Chain Based on Neighbor Sampling and GraphAttention Mechanism [J]. Computer Science, 2024, 51(10): 218-226.
[12]	WU Jiawei, FANG Quan, HU Jun, QIAN Shengsheng. Pre-training of Heterogeneous Graph Neural Networks for Multi-label Document Classification [J]. Computer Science, 2024, 51(1): 143-149.
[13]	YANG Zhizhuo, XU Lingling, Zhang Hu, LI Ru. Answer Extraction Method for Reading Comprehension Based on Frame Semantics and GraphStructure [J]. Computer Science, 2023, 50(8): 170-176.
[14]	SHAN Xiaohuan, SONG Rui, LI Haihai, SONG Baoyan. Event Recommendation Method with Multi-factor Feature Fusion in EBSN [J]. Computer Science, 2023, 50(7): 60-65.
[15]	ZHANG Tao, CHENG Yifei, SUN Xinxu. Graph Attention Networks Based on Causal Inference [J]. Computer Science, 2023, 50(6A): 220600230-9.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Social Bots Detection Based on Multi-relationship Graph Attention Network

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0