WalDis: Mining Discriminative Patterns within Dynamic Graphs

D 2018

WalDis: Mining Discriminative Patterns within Dynamic Graphs

VACULÍK, Karel and Lubomír POPELÍNSKÝ

Basic information

Original name

WalDis: Mining Discriminative Patterns within Dynamic Graphs

Authors

VACULÍK, Karel (203 Czech Republic, guarantor, belonging to the institution) and Lubomír POPELÍNSKÝ (203 Czech Republic, belonging to the institution)

Edition

NY, USA, IDEAS '18 Proceedings of the 22nd International Database Engineering & Applications Symposium, p. 95-102, 8 pp. 2018

Publisher

ACM New York

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/18:00103236

Organization unit

Faculty of Informatics

ISBN

978-1-4503-6527-7

DOI

http://dx.doi.org/10.1145/3216122.3216172

Keywords in English

data mining;discriminative patterns;dynamic graphs;graph mining;pattern mining;random walk

Abstract

V originále

Real-world networks typically evolve through time, which means there are various events occurring, such as edge additions or attribute changes. In order to understand the events, one must be able to discriminate between different events. Existing approaches typically discriminate whole graphs, which are, in addition, mostly static. We propose a new algorithm WalDis for mining discriminate patterns of events in dynamic graphs. This algorithm uses sampling by random walks and greedy approaches in order to keep the performance high. Furthermore, it does not require the time to be discretized as other algorithms commonly do. We have evaluated the algorithm on three real-world graph datasets.

Links

MUNI/A/0854/2017, interní kód MU

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

Investor: Masaryk University, Category A

MUNI/A/1038/2017, interní kód MU

Name: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 18

Investor: Masaryk University, Category A

Citovat

VACULÍK, Karel and Lubomír POPELÍNSKÝ. WalDis: Mining Discriminative Patterns within Dynamic Graphs. Online. In IDEAS '18 Proceedings of the 22nd International Database Engineering & Applications Symposium. NY, USA: ACM New York, 2018, p. 95-102. ISBN 978-1-4503-6527-7. Available from: https://dx.doi.org/10.1145/3216122.3216172.

@inproceedings{1423306,
   author = {Vaculík, Karel and Popelínský, Lubomír},
   address = {NY, USA},
   booktitle = {IDEAS '18 Proceedings of the 22nd International Database Engineering & Applications Symposium},
   doi = {http://dx.doi.org/10.1145/3216122.3216172},
   keywords = {data mining;discriminative patterns;dynamic graphs;graph mining;pattern mining;random walk},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {NY, USA},
   isbn = {978-1-4503-6527-7},
   pages = {95-102},
   publisher = {ACM New York},
   title = {WalDis: Mining Discriminative Patterns within Dynamic Graphs},
   url = {https://dl.acm.org/citation.cfm?id=3216172},
   year = {2018}
}

TY  - JOUR
ID  - 1423306
AU  - Vaculík, Karel - Popelínský, Lubomír
PY  - 2018
TI  - WalDis: Mining Discriminative Patterns within Dynamic Graphs
PB  - ACM New York
CY  - NY, USA
SN  - 9781450365277
KW  - data mining;discriminative patterns;dynamic graphs;graph mining;pattern mining;random walk
UR  - https://dl.acm.org/citation.cfm?id=3216172
N2  - Real-world networks typically evolve through time, which means there are various events occurring, such as edge additions or attribute changes. In order to understand the events, one must be able to discriminate between different events. Existing approaches typically discriminate whole graphs, which are, in addition, mostly static. We propose a new algorithm WalDis for mining discriminate patterns of events in dynamic graphs. This algorithm uses sampling by random walks and greedy approaches in order to keep the performance high. Furthermore, it does not require the time to be discretized as other algorithms commonly do. We have evaluated the algorithm on three real-world graph datasets.
ER  -

VACULÍK, Karel and Lubomír POPELÍNSKÝ. WalDis: Mining Discriminative Patterns within Dynamic Graphs. Online. In \textit{IDEAS '18 Proceedings of the 22nd International Database Engineering \&{} Applications Symposium}. NY, USA: ACM New York, 2018, p.~95-102. ISBN~978-1-4503-6527-7. Available from: https://dx.doi.org/10.1145/3216122.3216172.

Detailed Information on Publication Record