VACULÍK, Karel and Lubomír POPELÍNSKÝ. DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs. In Boström, H., Knobbe, A., Soares, C., Papapetrou, P.. Advances in Intelligent Data Analysis XV  15th International Symposium, IDA 2016. Neuveden: LNCS 9897, Springer, 2016. p. 308319, 12 pp. ISBN 9783319463483. doi:10.1007/9783319463490_27. 
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@inproceedings{1358700, author = {Vaculík, Karel and Popelínský, Lubomír}, address = {Neuveden}, booktitle = {Advances in Intelligent Data Analysis XV  15th International Symposium, IDA 2016}, doi = {http://dx.doi.org/10.1007/9783319463490_27}, editor = {Boström, H., Knobbe, A., Soares, C., Papapetrou, P.}, keywords = {graph mining; data mining; dynamic graphs; rule mining; anomaly detection; outlier detection; anomaly explanation}, howpublished = {tištěná verze "print"}, language = {eng}, location = {Neuveden}, isbn = {9783319463483}, pages = {308319}, publisher = {LNCS 9897, Springer}, title = {DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs}, year = {2016} }
TY  JOUR ID  1358700 AU  Vaculík, Karel  Popelínský, Lubomír PY  2016 TI  DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs PB  LNCS 9897, Springer CY  Neuveden SN  9783319463483 KW  graph mining KW  data mining KW  dynamic graphs KW  rule mining KW  anomaly detection KW  outlier detection KW  anomaly explanation N2  Ubiquitous network data has given rise to diverse graph mining and analytical methods. One of the graph mining domains is anomaly detection in dynamic graphs, which can be employed for fraud detection, network intrusion detection, suspicious behaviour identification, etc. Most existing methods search for anomalies rather on the global level of the graphs. In this work, we propose a new anomaly detection and explanation algorithm for dynamic graphs. The algorithm searches for anomaly patterns in the form of predictive rules that enable us to examine the evolution of dynamic graphs on the level of subgraphs. Specifically, these patterns are able to capture addition and deletion of vertices and edges, and relabeling of vertices and edges. In addition, the algorithm outputs normal patterns that serve as an explanation for the anomaly patterns. The algorithm has been evaluated on two realworld datasets. ER 
VACULÍK, Karel and Lubomír POPELÍNSKÝ. \textit{DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs}. In Boström, H., Knobbe, A., Soares, C., Papapetrou, P.. \textit{Advances in Intelligent Data Analysis XV  15th International Symposium, IDA 2016}. Neuveden: LNCS 9897, Springer, 2016. p.~308319, 12 pp. ISBN~9783319463483. doi:10.1007/9783319463490\_{}27.
