Fetching the paper…
Reading the bibliography…
Real-world graphs are complex to process for performing effective analysis, such as anomaly detection.
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
2000
Earlier work this paper cites.
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
2001
Earlier work this paper cites.
D. Chakrabarti, “Autopart: Parameter-free graph partitioning and outlier detection,” in PKDD , 2004
2004
Earlier work this paper cites.
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
2004
Earlier work this paper cites.
C. E. Priebe, J. M. Conroy, D. J. Marchette, and Y. Park, “Scan statistics on enron graphs,” Computational & Mathematical Organization Theory , 2005
2005
Earlier work this paper cites.
C. Liu, X. Yan, H. Yu, J. Han, and P. S. Yu, “Mining behavior graphs for ”backtrace” of noncrashing bugs,” in SDM , 2005
2005
Earlier work this paper cites.
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.–
2005
Earlier work this paper cites.
J. Sun, D. Tao, and C. Faloutsos, “Beyond streams and graphs: Dynamic tensor analysis,” in KDD , 2006
2006
Earlier work this paper cites.
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
2007
Earlier work this paper cites.
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
2007
Earlier work this paper cites.
J. Sun, Y. Xie, H. Zhang, and C. Faloutsos, “Less is more: Compact matrix decomposition for large sparse graphs,” in ICDM , 2007
2007
Earlier work this paper cites.
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
2007
Earlier work this paper cites.
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
2008
Earlier work this paper cites.
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
2008
Earlier work this paper cites.
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
2009
Earlier work this paper cites.
L. Akoglu, M. McGlohon, and C. Faloutsos, “Oddball: Spotting anomalies in weighted graphs,” in PAKDD , 2010
2010
Earlier work this paper cites.
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
2010
Earlier work this paper cites.
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
2010
Earlier work this paper cites.
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
2010
Earlier work this paper cites.
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
2010
Earlier work this paper cites.
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
2011
Earlier work this paper cites.
M. Davis, W. Liu, P. Miller, and G. Redpath, “Detecting anomalies in graphs with numeric labels,” in CIKM , 2011
2011
Earlier work this paper cites.
C. C. Aggarwal, “On classification of graph streams,” in Proceedings of the 2011 SIAM International Conference on Data Mining . SIAM, 2011, pp. 652–663
2011
Earlier work this paper cites.
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
2011
Earlier work this paper cites.
C. C. Aggarwal, Y. Zhao, and S. Y. Philip, “Outlier detection in graph streams,” in ICDE , 2011
2011
Earlier work this paper cites.
H. Tong and C.-Y. Lin, “Non-negative residual matrix factorization: Problem definition, fast solutions, and applications,” SDM , 2012
2012
Earlier work this paper cites.
Q. Ding, N. Katenka, P. Barford, E. Kolaczyk, and M. Crovella, “Intrusion as (anti)social communication: Characterization and detection,” in KDD , 2012
2012
Earlier work this paper cites.
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
2012
Earlier work this paper cites.
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
2012
Earlier work this paper cites.
E. Müller, P. I. Sánchez, Y. Mülle, and K. Böhm, “Ranking outlier nodes in subspaces of attributed graphs,” in ICDE , 2013
2013
Earlier work this paper cites.
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
2013
Earlier work this paper cites.
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
2013
Earlier work this paper cites.
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
2013
Earlier work this paper cites.
W. Yu, C. C. Aggarwal, S. Ma, and H. Wang, “On anomalous hotspot discovery in graph streams,” in ICDM , 2013
2013
Earlier work this paper cites.
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
2013
Earlier work this paper cites.
J. Neil, C. Hash, A. Brugh, M. Fisk, and C. B. Storlie, “Scan statistics for the online detection of locally anomalous subgraphs,” Technometrics , 2013
2013
Earlier work this paper cites.
D. Savage, X. Zhang, X. Yu, P. Chou, and Q. Wang, “Anomaly detection in online social networks,” Social Networks , 2014
2014
Earlier work this paper cites.
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
2014
Earlier work this paper cites.
B. Perozzi, L. Akoglu, P. Iglesias Sánchez, and E. Müller, “Focused clustering and outlier detection in large attributed graphs,” in KDD , 2014
2014
Earlier work this paper cites.
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
2014
Earlier work this paper cites.
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
2014
Earlier work this paper cites.
F. Chen and D. B. Neill, “Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs,” in KDD , 2014
2014
Cited alongside, same era.
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
2014
Cited alongside, same era.
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
2014
Cited alongside, same era.
S. Agrawal and J. Agrawal, “Survey on anomaly detection using data mining techniques,” Procedia Computer Science , 2015
2015
Cited alongside, same era.
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
2019
Later among the works it cites.
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
2019
Later among the works it cites.
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
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
2015
Cited alongside, same era.
L. Akoglu, H. Tong, and D. Koutra, “Graph based anomaly detection and description: A survey,” Data Min. Knowl. Discov. , 2015
2015
Cited alongside, same era.
L. Peel and A. Clauset, “Detecting change points in the large-scale structure of evolving networks,” in AAAI , 2015
2015
Cited alongside, same era.
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
2015
Cited alongside, same era.
M. Ahmed, A. Naser Mahmood, and J. Hu, “A survey of network anomaly detection techniques,” Journal of Network and Computer Applications , 2016
2016
Cited alongside, same era.
R. Yu, H. Qiu, Z. Wen, C. Lin, and Y. Liu, “A survey on social media anomaly detection,” SIGKDD Explor. Newsl. , 2016
2016
Cited alongside, same era.
R. Kaur and S. Singh, “A survey of data mining and social network analysis based anomaly detection techniques,” Egyptian Informatics Journal , 2016
2016
Cited alongside, same era.
M. Jiang, P. Cui, A. Beutel, C. Faloutsos, and S. Yang, “Catching synchronized behaviors in large networks: A graph mining approach,” KDD , 2016
2016
Cited alongside, same era.
2019
Later among the works it cites.
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
2020
Later among the works it cites.
R. Paudel and W. Eberle, “Snapsketch: Graph representation approach for intrusion detection in a streaming graph,” in MLG , 2020
2020
Later among the works it cites.
S. Bhatia, B. Hooi, M. Yoon, K. Shin, and C. Faloutsos, “Midas: Microcluster-based detector of anomalies in edge streams,” in AAAI , 2020
2020
Later among the works it cites.
C. Belth, X. Zheng, and D. Koutra, “Mining persistent activity in continually evolving networks,” in KDD , 2020
2020
Later among the works it cites.
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
2020
Later among the works it cites.
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
2020
Later among the works it cites.
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
2020
Later among the works it cites.
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
2020
Later among the works it cites.
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
2020
Later among the works it cites.
Z. Liu, C. Chen, X. Yang, J. Zhou, X. Li, and L. Song, “Heterogeneous graph neural networks for malicious account detection,” 2020
2020
Later among the works it cites.
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
2020
Later among the works it cites.
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
2020
Later among the works it cites.
2020
Later among the works it cites.
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
2020
Later among the works it cites.
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
2020
Later among the works it cites.
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
2020
Later among the works it cites.
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
2020
Later among the works it cites.
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
2020
Later among the works it cites.
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
2021
Later among the works it cites.
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
2021
Later among the works it cites.
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
2021
Later among the works it cites.
2021
Later among the works it cites.
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
2021
Later among the works it cites.
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
2021
Later among the works it cites.
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
2021
Later among the works it cites.
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
2021
Later among the works it cites.
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
2021
Later among the works it cites.
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
2022
Later among the works it cites.
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
2022
Later among the works it cites.
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
2022
Later among the works it cites.
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
2022
Later among the works it cites.
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
2022
Later among the works it cites.
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
2022
Later among the works it cites.
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
2022
Later among the works it cites.
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
2023
Later among the works it cites.
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
2023
Later among the works it cites.
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
2023
Later among the works it cites.