DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs

D 2016

DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs

VACULÍK, Karel a Lubomír POPELÍNSKÝ

Základní údaje

Originální název

DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs

Autoři

VACULÍK, Karel (203 Česká republika, garant, domácí) a Lubomír POPELÍNSKÝ (203 Česká republika, domácí)

Vydání

Neuveden, Advances in Intelligent Data Analysis XV - 15th International Symposium, IDA 2016, od s. 308-319, 12 s. 2016

Nakladatel

LNCS 9897, Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/16:00091413

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-46348-3

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-46349-0_27

UT WoS

000388259100027

Klíčová slova anglicky

graph mining; data mining; dynamic graphs; rule mining; anomaly detection; outlier detection; anomaly explanation

Štítky

core_A, firank_A

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 5. 2020 19:16, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

MUNI/A/0935/2015, interní kód MU

Název: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Akronym: SKOMU)

Investor: Masarykova univerzita, Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

MUNI/A/0945/2015, interní kód MU

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

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace V., DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

VACULÍK, Karel a 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, s. 308-319. ISBN 978-3-319-46348-3. Dostupné z: 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 a 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, s.~308-319. ISBN~978-3-319-46348-3. Dostupné z: https://dx.doi.org/10.1007/978-3-319-46349-0\_{}27.

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