F. Y. Edgeworth, “On discordant observations,” Philosophical Magazine, vol. 23, pp. 364-375, 1887.##
|
D. Toshniwal and S. Yadav, “Adaptive Outlier Detection in Streaming Time Series,” In Proceedings of International Conference on Asia Agriculture and Animal, ICAAA, 2011.##
|
|
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Computing Surveys (CSUR), vol. 41, pp. 1-58, 2009.##
|
|
V. Hodge and Austin, “A survey of outlier detection methodologies,” Intell, vol, 22, pp. 85–126, 2004.##
|
|
L. Akoglu, H. Tong, and D. Koutra, “Graph based anomaly detection and description: a survey,” Data Mining and Knowledge Discovery, vol. 29, pp. 626-688, 2014.##
|
|
K. Beyer, J. Goldstien, R. Ramakrishnan, and U. Shaft, “When Is "Nearest Neighbor" Meaningful?,” International Conference on Database Theory, pp. 217-235, 1999.##
|
|
J. Gao, F. Liang, W. Fan, C. Wang, and Y. Sun, “On community outliers and their efficient detection in information networks,” in KDD, 2010.##
|
|
J. Li, H. Dani, X. Hu, and H. Liu, “Radar: Residual Analysis for Anomaly Detection in Attributed Networks,” In Internationl Joint Conference on Artificial Intelligence, pp. 2152-2158, 2017.##
|
|
M. Davis, W. Liu, P. Miller, and G. Redpath, “Detecting Anomalies in Graphs with Numeric Labels,” In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1197-1202, 2011.##
|
|
T. Ji, J. Gao, and D. Yang, “A Scalable Algorithm for Detecting Community Outliers in Social Networks,” In International Conference on Web-Age Information Management, pp. 434-445, 2012.##
|
|
W. Yang, G. W. Shen, W. Wang, L. Y. Gong, and M. Yu, “Anomaly detection in microblogging via co-clustering,” Journal of Computer Science and Technology, pp. 1097–1108, 2015.##
|
|
C. C. Noble and D. J. Cook, “Graph-based anomaly detection,” In Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 631-636, 2003.##
|
|
P. I. Sánchez, E. Müller, F. Laforet, and F. Keller, “Statistical Selection of Congruent Subspaces for Mining Attributed Graphs,” In IEEE 13th International Conference on Data Mining, pp. 647–656, 2013.##
|
|
E. Müller, P. I. Sánchez, Y. Mülle, and K. Böhm, “Ranking outlier nodes in subspaces of attributed graphs,” In IEEE 29th International Conference on Data Engineering Workshops (ICDEW), 2013.##
|
|
P. I. Sánchez, E. Müller, O. Irmler, and K. Böhm, “Local context selection for outlier ranking in graphs with multiple numeric node attributes,” In Proceedings of the 26th International Conference on Scientific and Statistical, 2014.##
|
|
M. A. Prado-Romero and A. Gago-Alonso, “Community Feature Selection for Anomaly Detection in Attributed Graphs,” In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 109-116, 2017.##
|
|
V. D. Blondel, J. Guillaume, R. Lambiotte, and Lef, “Fast unfolding of communities in large networks,” 2008.##
|
|
X. He, D. Cai, and P. Niyogi, “Laplacian Score for Feature Selection,” In Proceedings of the 18th International Conference on Neural Information Processing and Statistical, pp. 507-514, 2005.##
|
|
X. Xu, N. Yuruk, Z. Feng, and T. A. Schweiger, “Scan: a structural clustering algorithm for networks,” In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 824-833, 2007.##
|
|
M. Breunig, H. Kriegel, R. Ng, J. Sander, and et al, “LOF: identifying density-based local outliers,” In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104, 2000.##
|
|
B. Perozzi and L. Akoglu, “Scalable Anomaly Ranking of Attributed Neighborhoods,” In SIAM International Conference on Data Mining, 2016.##
|
)
