[1] Wang, C., Feng, Z., Zhang, K., & Zhang, F. (2025). Self-supervised deep clustering for spammer group detection, Applied Intelligence, vol. 55, no. 11, p. 768.
[2] Zhang, Y. (2025). Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks, in Proc. 5th Int. Conf. on Artificial Intelligence and Industrial Technology Applications (AIITA), pp. 1197–1201, IEEE.
[3] He, C., Cheng, J., Chen ,G., Guan, Q., Fei, X., & Tang, Y. (2024). Detecting communities with multiple topics in attributed networks via self-supervised adaptive graph convolutional network, Information Fusion, vol. 105, p. 102254.
[4] Sun, X., Cheng, H., Liu, B., Li, J., Chen, H., Xu, G., & Yin, H. (2023). Self-supervised hypergraph representation learning for sociological analysis, IEEE Trans. on Knowledge and Data Engineering.
[5] De Sapienza Luna, R. (2023). Community detection on node-attributed networks using self-learning and graph neural networks, Ph.D. dissertation, Universidade Federal do Rio de Janeiro.
[6] He, C., Cheng, J., Chen, G., & Tang, Y. (2023). Multiple Topics Community Detection in Attributed Networks, in Proc. 46th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 2199–2203.
[7] Wu, P., Wang, W., Chang, F., Liu, C., & Wang, B. (2023). Dss-net: Dynamic self-supervised network for video anomaly detection, IEEE Trans. on Multimedia.
[8] Yu, R., He, X., & Liu, Y. (2015). GLAD, ACM Trans. Knowl. Discov. Data, vol. 10, no. 2, pp. 1–22, doi: 10.1145/2811268.
[9] Xiong, L., Póczos, B., Schneider, J., Connolly, A., & VanderPlas, J. (2011). Hierarchical probabilistic models for group anomaly detection, in Proc. 14th Int. Conf. on Artificial Intelligence and Statistics, pp. 789–797, JMLR W&CP.
[10] Xiong, L., Póczos, B., & Schneider, J. (2011). Group anomaly detection using flexible genre models, Advances in Neural Information Processing Systems, vol. 24.
[11] Muandet, K. & Schölkopf, B. (2013). One-class support measure machines for group anomaly detection, arXiv preprint arXiv:1303.0309.
[12] Singh, N., Miller, B. A., Bliss, N. T., & Wolfe, P. J. (2011). Anomalous subgraph detection via sparse principal component analysis, in Proc. IEEE Statistical Signal Processing Workshop (SSP), pp. 485–488, IEEE.
[13] Gupta, M., Mallya, A., Roy, S., Cho, J. H., & Han, J. (2014). Local learning for mining outlier subgraphs from network datasets, in Proc. 2014 SIAM Int. Conf. on Data Mining, pp. 73–81..
[14] Miller, B. A., Beard, M. S., Wolfe, P. J., & Bliss, N. T. (2015). A spectral framework for anomalous subgraph detection, IEEE Trans. Signal Processing, vol. 63, no. 16, pp. 4191–4206.
[15] Bridges, R. A., Collins, J. P., Ferragut, E. M., Laska, J. A., & Sullivan, B. D. (2015). Multi-level anomaly detection on time-varying graph data, in Proc. IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining (ASONAM), pp. 579–583.
[16] Zhao, J., Li , J., Zhou, B., F., Chen, Tomchik, P., & Ju, W. (2017). Parallel algorithms for anomalous subgraph detection, Concurrency and Computation: Practice and Experience, vol. 29, no. 3, p. e3769.
[17] Bansal, M. & Sharma, D. (2020). Ranking and discovering anomalous neighborhoods in attributed multiplex networks, in Proc. 7th ACM IKDD CoDS and 25th COMAD, pp. 46–54.
[18] Luan, M., Wang, B., Zhao, Y., & Hu, F. (2021). Anomalous subgraph detection in given expected degree networks with deep learning, IEEE Access, vol. 9, pp. 60052–60062.
[19] Safdari, H. & De Bacco, C. (2022). Anomaly detection and community detection in networks, Journal of Big Data, vol. 9, no. 1, p. 122.
[20] Lapid, S., Kagan, D., & Fire, M. (2023). Co-membership-based generic anomalous communities detection, Neural Processing Letters, vol. 55, no. 5, pp. 5619–5651.
[21] Perozzi, B. & Akoglu, L. (2018). Discovering communities and anomalies in attributed graphs: Interactive visual exploration and summarization, ACM Trans. Knowl. Discov. Data (TKDD), vol. 12, no. 2, pp. 1–40.
[22] Jiang, J., Chen, J., Gu, T., Choo, K. K. R., Liu, C., Yu, M., & Mohapatra, P. (2019). Anomaly detection with graph convolutional networks for insider threat and fraud detection, in Proc. MILCOM 2019 IEEE Military Communications Conf., pp. 109–114, IEEE.
[23] Qiao, F., Huang, C., Xu, S., & Qi, J. (2021). Community detection model based on graph representation and self-supervised learning, in Artificial Intelligence and Security: 7th Int. Conf., ICAIS 2021, Dublin, Ireland, Proc. Part I, pp. 27–40, Springer.
[24] Luo, X., Wu, J., Yang, J., Xue, S., Peng, H., Zhou, C., & Sheng, Q. Z. (2022). Deep graph level anomaly detection with contrastive learning, Scientific Reports, vol. 12, no. 1, p. 19867.
[25] Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., & Koutra, D. (2020). Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in neural information processing systems, 33, 7793-7804.
[26] Zheng, X., Wang, Y., Liu, Y., Li, M., Zhang, M., Jin, D., ... & Pan, S. (2022). Graph neural networks for graphs with heterophily: A survey. arXiv preprint arXiv:2202.07082.
[27] Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178-210.
[28] Yuan, X., Zhou, N., Yu, S., Huang, H., Chen, Z., & Xia, F. (2021, December). Higher-order structure based anomaly detection on attributed networks. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 2691-2700). IEEE.
[29] Cui, Y. L., Matan, O., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., Jacket, L. D., & Baird, H. S.(1990). Handwritten zip code recognition with multilayer networks, in Proc. Int. Conf. on Pattern Recognition, Atlantic City, NJ, USA, pp. –, IEEE.
[30] Stisen, A., Blunck, H., Bhattacharya, S., T. S., Prentow, Kjargaard, M. B., Den, A. K., Sonne, T., & Jensen, M. M. (2015). Smart devices are different: Assessing and mitigating mobile sensing heterogeneities for activity recognition, in Proc. ACM Conf. on Embedded Networked Sensor Systems, Seoul, Korea, ACM.
[31] Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004). RCV1: A new benchmark collection for text categorization research,” J. Mach. Learn. Res., vol. 5, pp. 361–397.
[32] Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019). Heterogeneous graph attention network, in Proc. WWW Conf., pp. 2022–2032.
[33] Alikhani, M., Chowdhury, S. N., De Melo, G., & Stone, M. (2019). CITE: A corpus of image-text discourse relations, arXiv preprint arXiv:1904.06286.
[34] Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008). Arnetminer: Extraction and mining of academic social networks, in Proc. 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 990–998, Aug.
