A genetic algorithm for discriminative graph pattern mining

D 2019

A genetic algorithm for discriminative graph pattern mining

VACULÍK, Karel a Lubomír POPELÍNSKÝ

Základní údaje

Originální název

A genetic algorithm for discriminative graph pattern mining

Autoři

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

Vydání

New York, Proceedings of the 23rd International Database Applications & Engineering Symposium, IDEAS 2019, Athens, Greece, od s. 461-462, 2 s. 2019

Nakladatel

ACM

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/19:00110952

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-6249-8

DOI

http://dx.doi.org/10.1145/3331076.3331113

UT WoS

000810167700045

Klíčová slova česky

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

Klíčová slova anglicky

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

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 5. 2024 16:23, 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 at- tribute changes. We propose a new algorithm for mining discriminative patterns of events in such dynamic graphs. This is dierent from other approaches, which typically discriminate whole static graphs while we focus on subgraphs that represent local events. Three tools have been employed The algorithm uses random walks and a nested genetic algo- rithm to nd the patterns through inexact matching. Furthermore, it does not require the time to be discretized as other algorithms commonly do. We have evaluated the algorithm on real-world graph data like DBLP and Enron. We show that the method outperforms baseline algorithm for all data sets and that the increase of accuracy is quite high, between 2.5for NIPS vs. KDD from DBLP dataset and 30% for Enron dataset. We also discus possible extensions of the algorithm.

Návaznosti

MUNI/A/1018/2018, interní kód MU

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

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

Citovat

VACULÍK, Karel a Lubomír POPELÍNSKÝ. A genetic algorithm for discriminative graph pattern mining. Online. In Bipin C. Desai and Dimosthenis Anagnostopoulos and Yannis Manolopoulos and Mara Nikolaidou. Proceedings of the 23rd International Database Applications & Engineering Symposium, IDEAS 2019, Athens, Greece. New York: ACM, 2019, s. 461-462. ISBN 978-1-4503-6249-8. Dostupné z: https://dx.doi.org/10.1145/3331076.3331113.

@inproceedings{1569319,
   author = {Vaculík, Karel and Popelínský, Lubomír},
   address = {New York},
   booktitle = {Proceedings of the 23rd International Database Applications & Engineering Symposium, IDEAS 2019, Athens, Greece},
   doi = {http://dx.doi.org/10.1145/3331076.3331113},
   editor = {Bipin C. Desai and Dimosthenis Anagnostopoulos and Yannis Manolopoulos and Mara Nikolaidou},
   keywords = {data mining; graph mining; dynamic graphs; pattern mining; discriminative patterns; random walk; genetic algorithm},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York},
   isbn = {978-1-4503-6249-8},
   pages = {461-462},
   publisher = {ACM},
   title = {A genetic algorithm for discriminative graph pattern mining},
   year = {2019}
}

TY  - JOUR
ID  - 1569319
AU  - Vaculík, Karel - Popelínský, Lubomír
PY  - 2019
TI  - A genetic algorithm for discriminative graph pattern mining
PB  - ACM
CY  - New York
SN  - 9781450362498
KW  - data mining
KW  - graph mining
KW  - dynamic graphs
KW  - pattern mining
KW  - discriminative patterns
KW  - random walk
KW  - genetic algorithm
N2  - Real-world networks typically evolve through time, which means there are various events occurring, such as edge additions or at- tribute changes. We propose a new algorithm for mining discriminative patterns of events in such dynamic graphs. This is dierent from other approaches, which typically discriminate whole static graphs while we focus on subgraphs that represent local events. Three tools have been employed The algorithm uses random walks and a nested genetic algo- rithm to nd the patterns through inexact matching. Furthermore, it does not require the time to be discretized as other algorithms commonly do. We have evaluated the algorithm on real-world graph data like DBLP and Enron. We show that the method outperforms baseline algorithm for all data sets and that the increase of accuracy is quite high, between 2.5for NIPS vs. KDD from DBLP dataset and 30% for Enron dataset. We also discus possible extensions of the algorithm.
ER  -

VACULÍK, Karel a Lubomír POPELÍNSKÝ. A genetic algorithm for discriminative graph pattern mining. Online. In Bipin C. Desai and Dimosthenis Anagnostopoulos and Yannis Manolopoulos and Mara Nikolaidou. \textit{Proceedings of the 23rd International Database Applications \&{} Engineering Symposium, IDEAS 2019, Athens, Greece}. New York: ACM, 2019, s.~461-462. ISBN~978-1-4503-6249-8. Dostupné z: https://dx.doi.org/10.1145/3331076.3331113.

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