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. 308-319, 12 pp. ISBN 978-3-319-46348-3. doi:10.1007/978-3-319-46349-0_27.
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Basic information
Original name DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs
Authors VACULÍK, Karel (203 Czech Republic, guarantor, belonging to the institution) and Lubomír POPELÍNSKÝ (203 Czech Republic, belonging to the institution).
Edition Neuveden, Advances in Intelligent Data Analysis XV - 15th International Symposium, IDA 2016, p. 308-319, 12 pp. 2016.
Publisher LNCS 9897, Springer
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
Impact factor Impact factor: 0.402 in 2005
RIV identification code RIV/00216224:14330/16:00091413
Organization unit Faculty of Informatics
ISBN 978-3-319-46348-3
ISSN 0302-9743
Doi http://dx.doi.org/10.1007/978-3-319-46349-0_27
UT WoS 000388259100027
Keywords in English graph mining; data mining; dynamic graphs; rule mining; anomaly detection; outlier detection; anomaly explanation
Tags core_A, firank_A
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 13/5/2020 19:16.
Abstract
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 real-world datasets.
Links
MUNI/A/0935/2015, internal MU codeName: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Acronym: SKOMU)
Investor: Masaryk University, Grant Agency of Masaryk University, Category A
MUNI/A/0945/2015, internal MU codeName: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace V.
Investor: Masaryk University, Grant Agency of Masaryk University, Category A
PrintDisplayed: 31/10/2020 12:44