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Neighborhood Difference-Enhanced Graph Neural Network Based on Hypergraph for Social Bot Detection

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Pattern Recognition and Computer Vision (PRCV 2024)

Abstract

Given the rise of social media, detecting social bots is crucial yet challenging. Despite state-of-the-art graph-based methods, bots can mimic real users by following many authentic accounts, significantly undermining the effectiveness of detection efforts. To tackle this, we propose NDE-GNN, a Neighborhood Difference-Enhanced Graph Neural Network based on Hypergraph. Utilizing hypergraphs, we extract user neighborhood representations, differentiate them with node features for distinct characteristics, and enhance these via Graph Neural Networks. Enhanced hypergraph features are fused with original graph features using a self-attention mechanism and feature fusion module. Fused features pass through a linear layer and softmax for detection. Experimental validation on real-world Twitter bot datasets confirms the superiority of NDE-GNN.

Shuhao Shi and Yan Li—Contribute equally to this work.

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References

  1. Ali Alhosseini, S., Bin Tareaf, R., Najafi, P., Meinel, C.: Detect me if you can: spam bot detection using inductive representation learning. In: Companion Proceedings of the 2019 World Wide Web Conference, pp. 148–153 (2019)

    Google Scholar 

  2. Bo, D., Wang, X., Shi, C., Shen, H.: Beyond low-frequency information in graph convolutional networks. In: AAAI Conference on Artificial Intelligence (2021)

    Google Scholar 

  3. Bo, D., Wang, X., Shi, C., Shen, H.: Beyond low-frequency information in graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3950–3957 (2021)

    Google Scholar 

  4. Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967)

    Article  Google Scholar 

  5. Cresci, S., Pietro, R.D., Petrocchi, M., Spognardi, A., Tesconi, M.: Fame for sale: efficient detection of fake twitter followers. Decis. Support Syst. 80, 56–71 (2015)

    Article  Google Scholar 

  6. Dong, Y., Ding, K., Jalaian, B., Ji, S., Li, J.: Adagnn: graph neural networks with adaptive frequency response filter. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 392–401 (2021)

    Google Scholar 

  7. Feng, F., Yang, Y., Cer, D.M., Arivazhagan, N., Wang, W.: Language-agnostic BERT sentence embedding. In: Annual Meeting of the Association for Computational Linguistics (2020)

    Google Scholar 

  8. Feng, S., Wan, H., Wang, N., Li, J., Luo, M.: Twibot-20: a comprehensive twitter bot detection benchmark. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 4485–4494 (2021)

    Google Scholar 

  9. Feng, S., Wan, H., Wang, N., Luo, M.: Botrgcn: Twitter bot detection with relational graph convolutional networks. In: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 236–239 (2021)

    Google Scholar 

  10. Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3558–3565 (2019)

    Google Scholar 

  11. Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)

    Google Scholar 

  12. Ivanov, S., Prokhorenkova, L.: Boost then convolve: gradient boosting meets graph neural networks (2021). arxiv:2101.08543)

  13. Kipf, T., Welling, M.: Semi-supervised classification with graph convolutional networks (2016). arxiv:1609.02907

  14. Klicpera, J., Bojchevski, A., Günnemann, S.: Predict then propagate: graph neural networks meet personalized Pagerank. In: International Conference on Learning Representations (2018)

    Google Scholar 

  15. Liu, S., Ying, R., Dong, H., Li, L., Xu, T., Rong, Y., Zhao, P., Huang, J., Wu, D.: Local augmentation for graph neural networks. In: International Conference on Machine Learning, pp. 14054–14072. PMLR (2022)

    Google Scholar 

  16. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized BERT pretraining approach (2019). arxiv:1907.11692

  17. Schlichtkrull, M., Kipf, T.N., Bloem, P., Van Den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: The semantic web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, Proceedings 15, pp. 593–607. Springer (2018)

    Google Scholar 

  18. Shi, S., Qiao, K., Chen, C., Yang, J., Chen, J., Yan, B.: Over-sampling strategy in feature space for graphs based class-imbalanced bot detection. In: Companion Proceedings of the ACM on Web Conference 2024, pp. 738–741 (2024)

    Google Scholar 

  19. Shi, S., Qiao, K., Chen, J., Yang, S., Yang, J., Song, B., Wang, L., Yan, B.: Mgtab: a multi-relational graph-based twitter account detection benchmark (2023). arxiv:2301.01123

  20. Shi, S., Qiao, K., Yang, J., Song, B., Chen, J., Yan, B.: Rf-gnn: Random forest boosted graph neural network for social bot detection (2023). arxiv:2304.08239

  21. Shi, S., Qiao, K., Yang, S., Wang, L., Chen, J., Yan, B.: Boosting-gnn: boosting algorithm for graph networks on imbalanced node classification. Front. Neurorobot. 15, 775688 (2021)

    Article  Google Scholar 

  22. Stringhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks. In: Proceedings of the 26th Annual Computer Security Applications Conference, pp. 1–9 (2010)

    Google Scholar 

  23. Sun, K., Lin, Z., Zhu, Z.: Adagcn: Adaboosting graph convolutional networks into deep models (2019). arxiv:1908.05081

  24. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio’, P., Bengio, Y.: Graph attention networks (2017). arxiv:1710.10903

  25. Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861–6871. PMLR (2019)

    Google Scholar 

  26. Xu, K., Li, C., Tian, Y., Sonobe, T., ichi Kawarabayashi, K., Jegelka, S.: Representation learning on graphs with jumping knowledge networks (2018). arxiv:1806.03536

  27. Ye, S., Tan, Z., Lei, Z., He, R., Wang, H., Zheng, Q., Luo, M.: Hofa: Twitter bot detection with homophily-oriented augmentation and frequency adaptive attention (2023). arxiv:2306.12870

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Correspondence to Bin Yan .

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Shi, S., Li, Y., Liu, Z., Chen, C., Chen, J., Yan, B. (2025). Neighborhood Difference-Enhanced Graph Neural Network Based on Hypergraph for Social Bot Detection. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15032. Springer, Singapore. https://doi.org/10.1007/978-981-97-8490-5_6

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  • DOI: https://doi.org/10.1007/978-981-97-8490-5_6

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