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Anomaly Detection in Graphs - Past, Present and Future.
Leman Akoglu, Mary McGlohon and Christos Faloutsos
In 2010, OddBall proposed a method to find unusual nodes in a large graph. First we present its main idea; then we list follow up work on node, subgraph, and graph level anomaly detection and representation learning; and conclude with future directions.
Associate Professor of Information Systems, Carnegie Mellon University.
Leman Akoglu is the Heinz College Dean's Associate Professor of Information Systems at Carnegie Mellon University. Her research focuses on pattern discovery and anomaly mining, with applications to fraud and event detection. She is a recipient of the SDM/IBM Early Career Data Mining Research award (2020), National Science Foundation CAREER award (2015) and US Army Research Office Young Investigator award (2013). Her research has won 7 publication awards (SIAM SDM 2019, ECML PKDD 2018, SIAM SDM 2016, SIAM SDM 2015, ADC 2014, PAKDD 2010, and ECML PKDD 2009).
Site Reliability Engineer, Google.
Mary McGlohon is a Site Reliability Engineer at Google, focused on productionization of large-scale machine learning systems and data pipelines. She completed her PhD in Machine Learning at Carnegie Mellon University in 2010, with thesis entitled "Structural Analysis of Networks: Observations and Applications".
Professor, Carnegie Mellon University.
Christos Faloutsos is a Professor at Carnegie Mellon University. He is an ACM Fellow, and the recipient of the Fredkin Professorship in Artificial Intelligence (2020); he has received the Research Contributions Award in ICDM 2006, the SIGKDD Innovations Award (2010), and the PAKDD Distinguished Contributions Award (2018). His research interests include large-scale data mining with emphasis on graphs and time sequences; anomaly detection, tensors, and fractals.
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