Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Anomaly Detection in Graph Structured Data: A Survey (2405.06172v1)

Published 10 May 2024 in cs.LG and cs.CR

Abstract: Real-world graphs are complex to process for performing effective analysis, such as anomaly detection. However, recently, there have been several research efforts addressing the issues surrounding graph-based anomaly detection. In this paper, we discuss a comprehensive overview of anomaly detection techniques on graph data. We also discuss the various application domains which use those anomaly detection techniques. We present a new taxonomy that categorizes the different state-of-the-art anomaly detection methods based on assumptions and techniques. Within each category, we discuss the fundamental research ideas that have been done to improve anomaly detection. We further discuss the advantages and disadvantages of current anomaly detection techniques. Finally, we present potential future research directions in anomaly detection on graph-structured data.

View on arXiv
Definition Search Book Streamline Icon: https://streamlinehq.com
References (120)
  1. S. Velampalli and W. Eberle, “Novel graph based anomaly detection using background knowledge,” in FLAIRS Conference, 2017.
  2. S. Agrawal and J. Agrawal, “Survey on anomaly detection using data mining techniques,” Procedia Computer Science, 2015.
  3. M. Ahmed, A. Naser Mahmood, and J. Hu, “A survey of network anomaly detection techniques,” Journal of Network and Computer Applications, 2016.
  4. R. A. Ariyaluran Habeeb, F. Nasaruddin, A. Gani, I. A. Targio Hashem, E. Ahmed, and M. Imran, “Real-time big data processing for anomaly detection: A survey,” International Journal of Information Management, 2019.
  5. N. Moustafa, J. Hu, and J. Slay, “A holistic review of network anomaly detection systems: A comprehensive survey,” Journal of Network and Computer Applications, 2019.
  6. D. Savage, X. Zhang, X. Yu, P. Chou, and Q. Wang, “Anomaly detection in online social networks,” Social Networks, 2014.
  7. S. Ranshous, S. Shen, D. Koutra, S. Harenberg, C. Faloutsos, and N. F. Samatova, “Anomaly detection in dynamic networks: A survey,” WIREs Comput. Stat., 2015.
  8. L. Akoglu, H. Tong, and D. Koutra, “Graph based anomaly detection and description: A survey,” Data Min. Knowl. Discov., 2015.
  9. R. Yu, H. Qiu, Z. Wen, C. Lin, and Y. Liu, “A survey on social media anomaly detection,” SIGKDD Explor. Newsl., 2016.
  10. R. Kaur and S. Singh, “A survey of data mining and social network analysis based anomaly detection techniques,” Egyptian Informatics Journal, 2016.
  11. J. Cadena, F. Chen, and A. Vullikanti, “Graph anomaly detection based on steiner connectivity and density,” Proceedings of the IEEE, 2018.
  12. T. Pourhabibi, K.-L. Ong, B. H. Kam, and Y. L. Boo, “Fraud detection: A systematic literature review of graph-based anomaly detection approaches,” Decision Support Systems, 2020.
  13. L. Akoglu, M. McGlohon, and C. Faloutsos, “Oddball: Spotting anomalies in weighted graphs,” in PAKDD, 2010.
  14. J. Gao, F. Liang, W. Fan, C. Wang, Y. Sun, and J. Han, “On community outliers and their efficient detection in information networks,” in KDD, 2010.
  15. H. Sun, J. Huang, J. Han, H. Deng, P. Zhao, and B. Feng, “gskeletonclu: Density-based network clustering via structure-connected tree division or agglomeration,” in 2010 IEEE International Conference on Data Mining, 2010.
  16. K. Henderson, B. Gallagher, L. Li, L. Akoglu, T. Eliassi-Rad, H. Tong, and C. Faloutsos, “It’s who you know: Graph mining using recursive structural features,” in KDD, 2011.
  17. M. Davis, W. Liu, P. Miller, and G. Redpath, “Detecting anomalies in graphs with numeric labels,” in CIKM, 2011.
  18. H. Tong and C.-Y. Lin, “Non-negative residual matrix factorization: Problem definition, fast solutions, and applications,” SDM, 2012.
  19. Q. Ding, N. Katenka, P. Barford, E. Kolaczyk, and M. Crovella, “Intrusion as (anti)social communication: Characterization and detection,” in KDD, 2012.
  20. E. Müller, P. I. Sánchez, Y. Mülle, and K. Böhm, “Ranking outlier nodes in subspaces of attributed graphs,” in ICDE, 2013.
  21. M. Gupta, A. Mallya, S. Roy, J. H. D. Cho, and J. Han, “Local learning for mining outlier subgraphs from network datasets,” in Proc. of the 2014 SIAM Intl. Conf. on Data Mining (SDM), 2014.
  22. B. Perozzi, L. Akoglu, P. Iglesias Sánchez, and E. Müller, “Focused clustering and outlier detection in large attributed graphs,” in KDD, 2014.
  23. M. Jiang, P. Cui, A. Beutel, C. Faloutsos, and S. Yang, “Catching synchronized behaviors in large networks: A graph mining approach,” KDD, 2016.
  24. B. Perozzi and L. Akoglu, “Scalable anomaly ranking of attributed neighborhoods,” SDM, 2016.
  25. B. Hooi, K. Shin, H. A. Song, A. Beutel, N. Shah, and C. Faloutsos, “Graph-based fraud detection in the face of camouflage,” KDD, 2017.
  26. K. Shin, T. Eliassi-Rad, and C. Faloutsos, “Corescope: Graph mining using k-core analysis — patterns, anomalies and algorithms,” in 2016 IEEE 16th International Conference on Data Mining (ICDM), 2016, pp. 469–478.
  27. C. C. Aggarwal, “On classification of graph streams,” in Proceedings of the 2011 SIAM International Conference on Data Mining.   SIAM, 2011, pp. 652–663.
  28. L. Chi, B. Li, and X. Zhu, “Fast graph stream classification using discriminative clique hashing,” in Pacific-Asia Conference on Knowledge Discovery and Data Mining.   Springer, 2013, pp. 225–236.
  29. J. Sun, D. Tao, and C. Faloutsos, “Beyond streams and graphs: Dynamic tensor analysis,” in KDD, 2006.
  30. J. Sun, C. Faloutsos, S. Papadimitriou, and P. S. Yu, “Graphscope: parameter-free mining of large time-evolving graphs,” in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’07.   New York, NY, USA: Association for Computing Machinery, 2007, p. 687–696. [Online]. Available: https://doi.org/10.1145/1281192.1281266
  31. M. Gupta, J. Gao, Y. Sun, and J. Han, “Integrating community matching and outlier detection for mining evolutionary community outliers,” in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’12.   New York, NY, USA: Association for Computing Machinery, 2012, p. 859–867. [Online]. Available: https://doi.org/10.1145/2339530.2339667
  32. M. Mardani, G. Mateos, and G. Giannakis, “Dynamic anomalography: Tracking network anomalies via sparsity and low rank,” IEEE Journal of Selected Topics in Signal Processing, 2013.
  33. A. Beutel, W. Xu, V. Guruswami, C. Palow, and C. Faloutsos, “Copycatch: Stopping group attacks by spotting lockstep behavior in social networks,” in WWW, 2013.
  34. K. Sricharan and K. Das, “Localizing anomalous changes in time-evolving graphs,” in Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD ’14.   New York, NY, USA: Association for Computing Machinery, 2014, p. 1347–1358. [Online]. Available: https://doi.org/10.1145/2588555.2612184
  35. H. Wang, M. Tang, Y. Park, and C. E. Priebe, “Locality statistics for anomaly detection in time series of graphs,” IEEE Transactions on Signal Processing, 2014.
  36. L. Peel and A. Clauset, “Detecting change points in the large-scale structure of evolving networks,” in AAAI, 2015.
  37. E. Manzoor, S. M. Milajerdi, and L. Akoglu, “Fast memory-efficient anomaly detection in streaming heterogeneous graphs,” in KDD, 2016.
  38. D. Koutra, N. Shah, J. T. Vogelstein, B. Gallagher, and C. Faloutsos, “D¡span class=”smallcaps smallercapital”¿elta¡/span¿c¡span class=”smallcaps smallercapital”¿on¡/span¿: Principled massive-graph similarity function with attribution,” ACM Trans. Knowl. Discov. Data, vol. 10, no. 3, feb 2016. [Online]. Available: https://doi.org/10.1145/2824443
  39. X. Teng, Y.-R. Lin, and X. Wen, “Anomaly detection in dynamic networks using multi-view time-series hypersphere learning,” in ACM on Conference on Information and Knowledge Management, 2017.
  40. W. Yu, C. C. Aggarwal, and W. Wang, “Temporally factorized network modeling for evolutionary network analysis,” in WSDM, 2017.
  41. D. Eswaran, C. Faloutsos, S. Guha, and N. Mishra, “Spotlight: Detecting anomalies in streaming graphs,” in KDD, 2018.
  42. L. Zheng, Z. Li, J. Li, Z. Li, and J. Gao, “Addgraph: Anomaly detection in dynamic graph using attention-based temporal gcn,” in IJCAI, 2019.
  43. M. Yoon, B. Hooi, K. Shin, and C. Faloutsos, “Fast and accurate anomaly detection in dynamic graphs with a two-pronged approach,” in KDD, 2019.
  44. R. Paudel and W. Eberle, “Snapsketch: Graph representation approach for intrusion detection in a streaming graph,” in MLG, 2020.
  45. W. Yu, C. C. Aggarwal, S. Ma, and H. Wang, “On anomalous hotspot discovery in graph streams,” in ICDM, 2013.
  46. K. Shin, B. Hooi, J. Kim, and C. Faloutsos, “Densealert: Incremental dense-subtensor detection in tensor streams,” in KDD, 2017.
  47. E. A. Manzoor, S. Momeni, V. N. Venkatakrishnan, and L. Akoglu, “Fast memory-efficient anomaly detection in streaming heterogeneous graphs,” in KDD, 2016.
  48. S. Ranshous, S. Harenberg, K. Sharma, and N. Samatova, “A scalable approach for outlier detection in edge streams using sketch-based approximations,” in ICDM, 2016.
  49. D. Eswaran and C. Faloutsos, “Sedanspot: Detecting anomalies in edge streams,” in ICDM, 2018.
  50. W. Yu, W. Cheng, C. C. Aggarwal, K. Zhang, H. Chen, and W. Wang, “Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks,” in KDD, 2018.
  51. S. Bhatia, B. Hooi, M. Yoon, K. Shin, and C. Faloutsos, “Midas: Microcluster-based detector of anomalies in edge streams,” in AAAI, 2020.
  52. C. Belth, X. Zheng, and D. Koutra, “Mining persistent activity in continually evolving networks,” in KDD, 2020.
  53. Y.-Y. Chang, P. Li, R. Sosic, M. H. Afifi, M. Schweighauser, and J. Leskovec, “F-fade: Frequency factorization for anomaly detection in edge streams,” in WSDM, 2021.
  54. S. Pandit, D. H. Chau, S. Wang, and C. Faloutsos, “Netprobe: a fast and scalable system for fraud detection in online auction networks,” in Proceedings of the 16th International Conference on World Wide Web, ser. WWW ’07.   New York, NY, USA: Association for Computing Machinery, 2007, p. 201–210. [Online]. Available: https://doi.org/10.1145/1242572.1242600
  55. M. Mongiovì, P. Bogdanov, R. Ranca, A. Singh, E. Papalexakis, and C. Faloutsos, “Netspot: Spotting significant anomalous regions on dynamic networks,” in Proceedings of the 13th SIAM International Conference on Data Mining, 05 2013. [Online]. Available: http://dx.doi.org/10.1137/1.9781611972832.4
  56. N. A. Heard, D. J. Weston, K. Platanioti, and D. J. Hand, “Bayesian anomaly detection methods for social networks,” The Annals of Applied Statistics, 2010.
  57. C. E. Priebe, J. M. Conroy, D. J. Marchette, and Y. Park, “Scan statistics on enron graphs,” Computational & Mathematical Organization Theory, 2005.
  58. J. Neil, C. Hash, A. Brugh, M. Fisk, and C. B. Storlie, “Scan statistics for the online detection of locally anomalous subgraphs,” Technometrics, 2013.
  59. F. Chen and D. B. Neill, “Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs,” in KDD, 2014.
  60. N. Shah, A. Beutel, B. Gallagher, and C. Faloutsos, “Spotting suspicious link behavior with fbox: An adversarial perspective,” in 2014 IEEE International Conference on Data Mining, 2014, pp. 959–964.
  61. M. Jiang, A. Beutel, P. Cui, B. Hooi, S. Yang, and C. Faloutsos, “A general suspiciousness metric for dense blocks in multimodal data,” in 2015 IEEE International Conference on Data Mining, 2015, pp. 781–786.
  62. K. Shin, B. Hooi, and C. Faloutsos, “M-zoom: Fast dense-block detection in tensors with quality guarantees,” in Machine Learning and Knowledge Discovery in Databases, P. Frasconi, N. Landwehr, G. Manco, and J. Vreeken, Eds.   Cham: Springer International Publishing, 2016, pp. 264–280.
  63. K. Shin, B. Hooi, J. Kim, and C. Faloutsos, “D-cube: Dense-block detection in terabyte-scale tensors,” in Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, ser. WSDM ’17.   New York, NY, USA: Association for Computing Machinery, 2017, p. 681–689. [Online]. Available: https://doi.org/10.1145/3018661.3018676
  64. S. Bhatia, A. Jain, P. Li, R. Kumar, and B. Hooi, “Mstream: Fast anomaly detection in multi-aspect streams,” in Proceedings of the Web Conference 2021, ser. WWW ’21.   New York, NY, USA: Association for Computing Machinery, 2021, p. 3371–3382. [Online]. Available: https://doi.org/10.1145/3442381.3450023
  65. P. B. Lamichhane and W. Eberle, “Anomaly detection in edge streams using term frequency-inverse graph frequency (tf-igf) concept,” in 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 661–667.
  66. R. Liu, S. Bhatia, and B. Hooi, “Isconna: Streaming anomaly detection with frequency and patterns,” ArXiv, vol. abs/2104.01632, 2021.
  67. S. Bhatia, R. Liu, B. Hooi, M. Yoon, K. Shin, and C. Faloutsos, “Real-time anomaly detection in edge streams,” ACM Trans. Knowl. Discov. Data, vol. 16, no. 4, jan 2022. [Online]. Available: https://doi.org/10.1145/3494564
  68. B. A. Prakash, M. Seshadri, A. Sridharan, S. Machiraju, and C. Faloutsos, “Eigenspokes: Surprising patterns and scalable community chipping in large graphs,” in 2009 IEEE International Conference on Data Mining Workshops, 2009, pp. 290–295.
  69. M. Jiang, P. Cui, A. Beutel, C. Faloutsos, and S. Yang, “Inferring strange behavior from connectivity pattern in social networks,” in Advances in Knowledge Discovery and Data Mining, V. S. Tseng, T. B. Ho, Z.-H. Zhou, A. L. P. Chen, and H.-Y. Kao, Eds.   Cham: Springer International Publishing, 2014, pp. 126–138.
  70. J. Sun, Y. Xie, H. Zhang, and C. Faloutsos, “Less is more: Compact matrix decomposition for large sparse graphs,” in ICDM, 2007.
  71. T. G. Kolda and J. Sun, “Scalable tensor decompositions for multi-aspect data mining,” in 2008 Eighth IEEE International Conference on Data Mining, 2008, pp. 363–372.
  72. F. Guo, K. Maruhashi, and C. Faloutsos, “Multiaspectforensics: Pattern mining on large-scale heterogeneous networks with tensor analysis,” in 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.   Los Alamitos, CA, USA: IEEE Computer Society, jul 2011, pp. 203–210. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/ASONAM.2011.80
  73. D. Koutra, E. E. Papalexakis, and C. Faloutsos, “Tensorsplat: Spotting latent anomalies in time,” in 2012 16th Panhellenic Conference on Informatics, 2012, pp. 144–149.
  74. D. Chakrabarti, “Autopart: Parameter-free graph partitioning and outlier detection,” in PKDD, 2004.
  75. C. Liu, X. Yan, H. Yu, J. Han, and P. S. Yu, “Mining behavior graphs for ”backtrace” of noncrashing bugs,” in SDM, 2005.
  76. A. Ghoting, M. Otey, and S. Parthasarathy, “Loaded: link-based outlier and anomaly detection in evolving data sets,” in Fourth IEEE International Conference on Data Mining (ICDM’04), 2004, pp. 387–390.
  77. J. Sun, H. Qu, D. Chakrabarti, and C. Faloutsos, “Neighborhood formation and anomaly detection in bipartite graphs,” in Fifth IEEE International Conference on Data Mining (ICDM’05), 2005, pp. 8 pp.–.
  78. H. D. K. Moonesinghe and P.-N. Tan, “Outrank: a graph-based outlier detection framework using random walk,” Int. J. Artif. Intell. Tools, vol. 17, pp. 19–36, 2008.
  79. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “Lof: Identifying density-based local outliers,” in Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD ’00.   New York, NY, USA: Association for Computing Machinery, 2000, p. 93–104.
  80. W. Jin, A. K. H. Tung, and J. Han, “Mining top-n local outliers in large databases,” in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’01.   New York, NY, USA: Association for Computing Machinery, 2001, p. 293–298.
  81. C. C. Aggarwal, Y. Zhao, and S. Y. Philip, “Outlier detection in graph streams,” in ICDE, 2011.
  82. W. Eberle and L. Holder, “Discovering structural anomalies in graph-based data,” in Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), 2007, pp. 393–398.
  83. R. Paudel, T. Muncy, and W. Eberle, “Detecting dos attack in smart home iot devices using a graph-based approach,” in 2019 IEEE International Conference on Big Data (Big Data), 2019, pp. 5249–5258.
  84. K. Henderson, T. Eliassi-Rad, C. Faloutsos, L. Akoglu, L. Li, K. Maruhashi, B. A. Prakash, and H. Tong, “Metric forensics: A multi-level approach for mining volatile graphs,” in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’10.   New York, NY, USA: Association for Computing Machinery, 2010, p. 163–172. [Online]. Available: https://doi.org/10.1145/1835804.1835828
  85. K. Ding, J. Li, R. Bhanushali, and H. Liu, “Deep anomaly detection on attributed networks,” in SIAM International Conference on Data Mining (SDM), 2019.
  86. Y. Li, X. Huang, J. Li, M. Du, and N. Zou, “Specae: Spectral autoencoder for anomaly detection in attributed networks,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, ser. CIKM ’19.   New York, NY, USA: Association for Computing Machinery, 2019, p. 2233–2236. [Online]. Available: https://doi.org/10.1145/3357384.3358074
  87. J. Wang, R. Wen, C. Wu, Y. Huang, and J. Xion, “Fdgars: Fraudster detection via graph convolutional networks in online app review system,” Companion Proceedings of The 2019 World Wide Web Conference, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:153314635
  88. M. Dong, B. Zheng, N. Quoc Viet Hung, H. Su, and G. Li, “Multiple rumor source detection with graph convolutional networks,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, ser. CIKM ’19.   New York, NY, USA: Association for Computing Machinery, 2019, p. 569–578. [Online]. Available: https://doi.org/10.1145/3357384.3357994
  89. D. Wang, Y. Qi, J. Lin, P. Cui, Q. Jia, Z. Wang, Y. Fang, Q. Yu, J. Zhou, and S. Yang, “A semi-supervised graph attentive network for financial fraud detection,” 2019 IEEE International Conference on Data Mining (ICDM), pp. 598–607, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:210994135
  90. Y. Wu, D. Lian, Y. Xu, L. Wu, and E. Chen, “Graph convolutional networks with markov random field reasoning for social spammer detection,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 01, pp. 1054–1061, Apr. 2020. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/5455
  91. L. Zhong, J. Cao, Q. Sheng, J. Guo, and Z. Wang, “Integrating semantic and structural information with graph convolutional network for controversy detection,” in Annual Meeting of the Association for Computational Linguistics, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:218674458
  92. S. Bandyopadhyay, L. N, S. V. Vivek, and M. N. Murty, “Outlier resistant unsupervised deep architectures for attributed network embedding,” in Proceedings of the 13th International Conference on Web Search and Data Mining, ser. WSDM ’20.   New York, NY, USA: Association for Computing Machinery, 2020, p. 25–33. [Online]. Available: https://doi.org/10.1145/3336191.3371788
  93. S. Zhang, H. Yin, T. Chen, Q. V. N. Hung, Z. Huang, and L. Cui, “Gcn-based user representation learning for unifying robust recommendation and fraudster detection,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, ser. SIGIR ’20.   New York, NY, USA: Association for Computing Machinery, 2020, p. 689–698. [Online]. Available: https://doi.org/10.1145/3397271.3401165
  94. Z. Liu, Y. Dou, P. S. Yu, Y. Deng, and H. Peng, “Alleviating the inconsistency problem of applying graph neural network to fraud detection,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, ser. SIGIR ’20.   New York, NY, USA: Association for Computing Machinery, 2020, p. 1569–1572. [Online]. Available: https://doi.org/10.1145/3397271.3401253
  95. Y. Liu, Z. Li, S. Pan, C. Gong, C. Zhou, and G. Karypis, “Anomaly detection on attributed networks via contrastive self-supervised learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 6, pp. 2378–2392, 2022.
  96. B. Chen, J. Zhang, X. Zhang, Y. Dong, J. Song, P. Zhang, K. Xu, E. Kharlamov, and J. Tang, “Gccad: Graph contrastive coding for anomaly detection,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 8, pp. 8037–8051, 2023.
  97. Z. Liu, C. Chen, X. Yang, J. Zhou, X. Li, and L. Song, “Heterogeneous graph neural networks for malicious account detection,” 2020.
  98. P. Zheng, S. Yuan, X. Wu, J. Li, and A. Lu, “One-class adversarial nets for fraud detection,” in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, ser. AAAI’19/IAAI’19/EAAI’19.   AAAI Press, 2019. [Online]. Available: https://doi.org/10.1609/aaai.v33i01.33011286
  99. Y.-J. Lu and C.-T. Li, “GCAN: Graph-aware co-attention networks for explainable fake news detection on social media,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, D. Jurafsky, J. Chai, N. Schluter, and J. Tetreault, Eds.   Online: Association for Computational Linguistics, Jul. 2020, pp. 505–514. [Online]. Available: https://aclanthology.org/2020.acl-main.48
  100. A. Deng and B. Hooi, “Graph neural network-based anomaly detection in multivariate time series,” in Proceedings of the AAAI conference on artificial intelligence, vol. 35, no. 5, 2021, pp. 4027–4035.
  101. Y. Liu, S. Pan, Y. Wang, F. Xiong, L. Wang, Q. Chen, and V. Lee, “Anomaly detection in dynamic graphs via transformer,” IEEE Transactions on Knowledge & Data Engineering, vol. 35, no. 12, pp. 12 081–12 094, dec 2023.
  102. D. Duan, L. Tong, Y. Li, J. Lu, L. Shi, and C. Zhang, “Aane: Anomaly aware network embedding for anomalous link detection,” in 2020 IEEE International Conference on Data Mining (ICDM), 2020, pp. 1002–1007.
  103. J. Song, X. Qu, Z. Hu, Z. Li, J. Gao, and J. Zhang, “A subgraph-based knowledge reasoning method for collective fraud detection in e-commerce,” Neurocomputing, vol. 461, pp. 587–597, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231221009632
  104. G. Zhang, Z. Li, J. Huang, J. Wu, C. Zhou, J. Yang, and J. Gao, “efraudcom: An e-commerce fraud detection system via competitive graph neural networks,” ACM Trans. Inf. Syst., vol. 40, no. 3, pp. 47:1–47:29, 2022. [Online]. Available: https://doi.org/10.1145/3474379
  105. L. Zheng, Z. Li, J. Li, Z. Li, and J. Gao, “Addgraph: Anomaly detection in dynamic graph using attention-based temporal gcn,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19.   International Joint Conferences on Artificial Intelligence Organization, 7 2019, pp. 4419–4425. [Online]. Available: https://doi.org/10.24963/ijcai.2019/614
  106. C. Yang, L. Zhou, H. Wen, Z. Zhou, and Y. Wu, “H-vgrae: A hierarchical stochastic spatial-temporal embedding method for robust anomaly detection in dynamic networks,” ArXiv, vol. abs/2007.06903, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:220514236
  107. D. Zhu, Y. Ma, and Y. Liu, “A flexible attentive temporal graph networks for anomaly detection in dynamic networks,” 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 870–875, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:231913623
  108. B. Wang, T. Hayashi, and Y. Ohsawa, “Hierarchical graph convolutional network for data evaluation of dynamic graphs,” 2020 IEEE International Conference on Big Data (Big Data), pp. 4475–4481, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:232373391
  109. T. Bian, X. Xiao, T. Xu, P. Zhao, W. Huang, Y. Rong, and J. Huang, “Rumor detection on social media with bi-directional graph convolutional networks,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 549–556, 04 2020.
  110. L. Cai, Z. Chen, C. Luo, J. Gui, J. Ni, D. Li, and H. Chen, “Structural temporal graph neural networks for anomaly detection in dynamic graphs,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, ser. CIKM ’21.   New York, NY, USA: Association for Computing Machinery, 2021, p. 3747–3756. [Online]. Available: https://doi.org/10.1145/3459637.3481955
  111. S. Wang, Z. Chen, X. Yu, D. Li, J. Ni, L.-A. Tang, J. Gui, Z. Li, H. Chen, and P. S. Yu, “Heterogeneous graph matching networks for unknown malware detection,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19.   International Joint Conferences on Artificial Intelligence Organization, 7 2019, pp. 3762–3770. [Online]. Available: https://doi.org/10.24963/ijcai.2019/522
  112. Y. Fan, Y. Ye, Q. Peng, J. Zhang, Y. Zhang, X. Xiao, C. Shi, Q. Xiong, F. Shao, and L. Zhao, “Metagraph aggregated heterogeneous graph neural network for illicit traded product identification in underground market,” in 2020 IEEE International Conference on Data Mining (ICDM), 2020, pp. 132–141.
  113. R. Ma, G. Pang, L. Chen, and A. van den Hengel, “Deep graph-level anomaly detection by glocal knowledge distillation,” in Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, ser. WSDM ’22.   New York, NY, USA: Association for Computing Machinery, 2022, p. 704–714. [Online]. Available: https://doi.org/10.1145/3488560.3498473
  114. C. Qiu, M. Kloft, S. Mandt, and M. R. Rudolph, “Raising the bar in graph-level anomaly detection,” in International Joint Conference on Artificial Intelligence, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:249152138
  115. L. Huang, Y. Zhu, Y. Gao, T. Liu, C. Chang, C. Liu, Y. Tang, and C.-D. Wang, “Hybrid-order anomaly detection on attributed networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12 249–12 263, 2023.
  116. L. Zhao and L. Akoglu, “On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights,” Big Data, vol. 11, no. 3, pp. 151–180, 2023, pMID: 34870450. [Online]. Available: https://doi.org/10.1089/big.2021.0069
  117. A. Markovitz, G. Sharir, I. Friedman, L. Zelnik-Manor, and S. Avidan, “Graph embedded pose clustering for anomaly detection,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 10 536–10 544.
  118. S. Ge, L. Cheng, and H. Liu, “Improving cyberbullying detection with user interaction,” in Proceedings of the Web Conference 2021, ser. WWW ’21.   New York, NY, USA: Association for Computing Machinery, 2021, p. 496–506. [Online]. Available: https://doi.org/10.1145/3442381.3449828
  119. P. B. Lamichhane and W. Eberle, “Self-organizing map-based graph clustering and visualization on streaming graphs,” in 2022 IEEE International Conference on Data Mining Workshops (ICDMW), 2022, pp. 706–713.
  120. P. B. Lamichhane, H. Mannering, and W. Eberle, “Discovering breach patterns on the internet of health things: A graph and machine learning anomaly analysis,” The International FLAIRS Conference Proceedings, vol. 35, May 2022. [Online]. Available: https://journals.flvc.org/FLAIRS/article/view/130628
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (2)
  1. Prabin B Lamichhane (1 paper)
  2. William Eberle (10 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now