Node re-ordering as a means of anomaly detection in time-evolving graphs

Rashidi, Lida, Kan, Andrey, Bailey, James, Chan, Jeffrey, Leckie, Christopher, Liu, Wei, Rajasegarar, Sutharshan and Ramamohanarao, Kotagiri 2016, Node re-ordering as a means of anomaly detection in time-evolving graphs, in ECML PKDD 2016 : Machine learning and knowledge discovery in databases : Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Springer, Cham, Switzerland, pp. 162-178, doi: 10.1007/978-3-319-46227-1_11.

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Title Node re-ordering as a means of anomaly detection in time-evolving graphs
Author(s) Rashidi, Lida
Kan, Andrey
Bailey, James
Chan, Jeffrey
Leckie, Christopher
Liu, Wei
Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan orcid.org/0000-0002-6559-6736
Ramamohanarao, Kotagiri
Conference name European Machine Learning and Data Mining. Conference (15th : 2016 : Riva del Garda, Italy)
Conference location Riva del Garda, Italy
Conference dates 2016/09/19 - 2016/09/23
Title of proceedings ECML PKDD 2016 : Machine learning and knowledge discovery in databases : Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Editor(s) Frasconi, Paolo
Landwehr, Niels
Manco, Giuseppe
Vreeken, Jilles
Publication date 2016
Series European Machine Learning and Data Mining Conference
Start page 162
End page 178
Total pages 17
Publisher Springer
Place of publication Cham, Switzerland
Keyword(s) Anomaly detection
Time-evolving graphs
Data
Nodes
ISBN 9783319462264
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-46227-1_11
Field of Research 080109 Pattern Recognition and Data Mining
08 Information And Computing Sciences
Socio Economic Objective 899999 Information and Communication Services not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2016, Springer International Publishing AG
Persistent URL http://hdl.handle.net/10536/DRO/DU:30087241

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