Causal Entropy–Driven Generative Adversarial Learning for Anomalous Event Detection in Social Networks

Tian Tian; Tianqi Chen

doi:10.54097/pm50s941

Authors

Tian Tian
Tianqi Chen

DOI:

https://doi.org/10.54097/pm50s941

Keywords:

Social Networks, Anomalous Event Detection, Generative Adversarial Learning, Long Short-Term Memory Networks, Causality Mining

Abstract

In social networks, anomalous events not only disrupt normal network evolution but also pose potential threats to network security. Therefore, achieving efficient anomaly detection in dynamically and semantically complex environments is of significant importance. However, existing methods often struggle to effectively capture the dynamic evolutionary relationships between events and their underlying causal dependencies. To address this, this paper proposes a causal entropy–driven generative adversarial learning for anomalous event detection in social networks. First, a log parser converts unstructured social network logs into structured events, which contain timestamps, IP addresses, and event content. Subsequently, we standardize causal entropy to measure the strength of causal relationships between events and construct causal sequences. Building upon this foundation, we design a generative adversarial framework. The generator combines a gated recurrent unit, a self-attention mechanism, and a long short-term memory network to capture deep event semantics and generate realistic log samples. The discriminator achieves precise differentiation between normal and abnormal events through mean cross-entropy loss on fully connected layers. Experimental results demonstrate that the proposed method achieves approximately 6.5% higher detection accuracy than existing approaches, validating its effectiveness and robustness in identifying anomaly events.

Downloads

Download data is not yet available.

References

[1] K. Z. Khanam, G. Srivastava, and V. Mago, ‘‘The homophily principle in social network analysis: A survey,’’ Multimedia Tools and Applications, vol. 82, no. 6, pp. 8811–8854, 2023.

[2] S. R. Sahoo and B. B. Gupta, ‘‘Multiple features based approach for automatic fake news detection on social networks using deep learning,’’ Applied Soft Computing, vol. 100, p. 106983, 2021.

[3] A. E. Ezugwu, A. M. Ikotun, O. O. Oyelade, L. Abualigah, J. O. Agushaka, C. I. Eke, and A. A. Akinyelu, ‘‘A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects,’’ Engineering Applications of Artificial Intelligence, vol. 110, p. 104743, 2022.

[4] A. Abdelrazek, Y. Eid, E. Gawish, W. Medhat, and A. Hassan, ‘‘Topic modeling algorithms and applications: A survey,’’ Information Systems, vol. 112, p. 102131, 2023.

[5] M. R. Islam, S. Liu, X. Wang, and G. Xu, ‘‘Deep learning for misinformation detection on online social networks: a survey and new perspectives,’’ Social Network Analysis and Mining, vol. 10, no. 1, p. 82, 2020.

[6] G. Nagarajan, R. Minu, and A. Jayanthila Devi, ‘‘Optimal nonparametric bayesian model-based multimodal bovw creation using multilayer plsa,’’ Circuits, Systems, and Signal Processing, vol. 39, no. 2, pp. 1123–1132, 2020.

[7] H. Jelodar, Y. Wang, C. Yuan, X. Feng, X. Jiang, Y. Li, and L. Zhao, “Latent dirichlet allocation (lda) and topic modeling: models, applications, a survey,” Multimedia tools and applications, vol. 78, pp. 15169–15211, 2019.

[8] H. Zhou, H. Yin, H. Zheng, and Y. Li, ‘‘A survey on multi-modal social event detection,’’ Knowledge-Based Systems, vol. 195, p. 105695, 2020.

[9] L. Zhang, B. Wu, and P. Dong, ‘‘Attribute graph anomaly detection utilizing memory networks enhanced by multi-embedding comparison,’’ Neurocomputing, vol. 633, p. 129762, 2025.

[10] S. Tabassum, F. S. Pereira, S. Fernandes, and J. Gama, ‘‘Social network analysis: An overview,’’ Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 5, p. e1256, 2018.

[11] G. Li and J. J. Jung, ‘‘Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges,’’ Information Fusion, vol. 91, pp. 93–102, 2023.

[12] Y. Zuo, J. Zhao, and K. Xu, ‘‘Word network topic model: a simple but general solution for short and imbalanced texts,’’ Knowledge and Information Systems, vol. 48, pp. 379–398, 2016.

[13] J. Zhang, M. Liu, and Y. Zhang, ‘‘Topic-informed neural approach for biomedical event extraction,’’ Artificial intelligence in medicine, vol. 103, p. 101783, 2020.

[14] G. Stilo and P. Velardi, ‘‘Efficient temporal mining of micro-blog texts and its application to event discovery,’’ Data Mining and Knowledge Discovery, vol. 30, no. 2, pp. 372–402, 2016.

[15] K. Bountrogiannis, G. Tzagkarakis, and P. Tsakalides, ‘‘Distribution agnostic symbolic representations for time series dimensionality reduction and online anomaly detection,’’ IEEE Transactions on Knowledge and Data Engineering, 2022.

[16] S. Li, H. Ji, and J. Han, ‘‘Document-level event argument extraction by conditional generation,’’ arXiv preprint arXiv:2104.05919, 2021.

[17] W. Xie, F. Zhu, J. Jiang, E.-P. Lim, and K. Wang, ‘‘Topicsketch: Real-time bursty topic detection from twitter,’’ IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 8, pp. 2216–2229, 2019.

[18] Q. Diao, J. Jiang, F. Zhu, and E. P. LIM, ‘‘Finding bursty topics from microblogs.’’ Acl, 2012.

[19] R. Wang, D. Zhou, and Y. He, ‘‘Atm: Adversarial-neural topic model,’’ Information Processing & Management, vol. 56, no. 6, p. 102098, 2019.

[20] A. P. Logan, P. M. LaCasse, and B. J. Lunday, ‘‘Social network analysis of twitter interactions: a directed multilayer network approach,’’ Social Network Analysis and Mining, vol. 13, no. 1, p. 65, 2023.

[21] G. Pu, L. Wang, J. Shen, and F. Dong, ‘‘A hybrid unsupervised clustering-based anomaly detection method,’’ Tsinghua Science and Technology, vol. 26, no. 2, pp. 146–153, 2020.

[22] L. Yan, C. Luo, and R. Shao, ‘‘Discrete log anomaly detection: a novel time-aware graph-based link prediction approach,’’ Information Sciences, vol. 647, p. 119576, 2023.

[23] E. O. Omuya, G. O. Okeyo, and M. W. Kimwele, ‘‘Feature selection for classification using principal component analysis and information gain,’’ Expert Systems with Applications, vol. 174, p. 114765, 2021.

[24] M. Gnouma, R. Ejbali, and M. Zaied, ‘‘Deep hashing and sparse representation of abnormal events detection,’’ The Computer Journal, vol. 67, no. 1, pp. 3–17, 2024.

[25] J. Sun, D. Taylor, and E. M. Bollt, ‘‘Causal network inference by optimal causation entropy,’’ SIAM Journal on Applied Dynamical Systems, vol. 14, no. 1, pp. 73–106, 2015.

[26] A. El Abdouli, A. Chaffai, L. Hassouni, H. Anoun, and K. Rifi, ‘‘Current morroccan trends in social networks,’’ International Journal of Computer Science and Information Security, vol. 14, no. 3, pp. 86–98, 2016.

[27] C. Wang, Y.-M. Zhang, and C.-L. Liu, ‘‘Anomaly detection via minimum likelihood generative adversarial networks,’’ in 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018, pp. 1121–1126.

[28] A. Guille and C. Favre, ‘‘Event detection, tracking, and visualization in twitter: a mention-anomaly-based approach,’’ Social Network Analysis and Mining, vol. 5, no. 1, p. 18, 2015.

[29] P. Y. Erfanian, B. R. Cami, and H. Hassanpour, ‘‘An evolutionary event detection model using the matrix decomposition oriented dirichlet process,’’ Expert Systems with Applications, vol. 189, p. 116086, 2022.

[30] A. Borg and M. Boldt, ‘‘Using vader sentiment and svm for predicting customer response sentiment,’’ Expert Systems with Applications, vol. 162, p. 113746, 2020.

[31] X. Chen, C. Cao, and J. Mai, ‘‘Network anomaly detection based on deep support vector data description,’’ in 2020 5th IEEE International Conference on Big Data Analytics (ICBDA). IEEE, 2020, pp. 251–255.