DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs

D 2016

DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs

VACULÍK, Karel and Lubomír POPELÍNSKÝ

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

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

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

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

Abstract

V originále

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, interní kód MU

Name: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Acronym: SKOMU)

Investor: Masaryk University, Category A

MUNI/A/0945/2015, interní kód MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace V.

Investor: Masaryk University, Category A

Citovat

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. ISBN 978-3-319-46348-3. Available from: https://dx.doi.org/10.1007/978-3-319-46349-0_27.

@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/10.1007/978-3-319-46349-0_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 = {978-3-319-46348-3},
   pages = {308-319},
   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 real-world datasets.
ER  -

VACULÍK, Karel and Lubomír POPELÍNSKÝ. 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.~308-319. ISBN~978-3-319-46348-3. Available from: https://dx.doi.org/10.1007/978-3-319-46349-0\_{}27.

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