WalDis: Mining Discriminative Patterns within Dynamic Graphs

D 2018

WalDis: Mining Discriminative Patterns within Dynamic Graphs

VACULÍK, Karel a Lubomír POPELÍNSKÝ

Základní údaje

Originální název

WalDis: Mining Discriminative Patterns within Dynamic Graphs

Autoři

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

Vydání

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

Nakladatel

ACM New York

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/18:00103236

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-6527-7

DOI

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

Klíčová slova anglicky

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

Štítky

core_B, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 31. 5. 2022 14:20, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

MUNI/A/0854/2017, interní kód MU

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

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

MUNI/A/1038/2017, interní kód MU

Název: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 18

Investor: Masarykova univerzita, Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 18, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

VACULÍK, Karel a 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, s. 95-102. ISBN 978-1-4503-6527-7. Dostupné z: 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 a 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, s.~95-102. ISBN~978-1-4503-6527-7. Dostupné z: https://dx.doi.org/10.1145/3216122.3216172.

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