A genetic algorithm for discriminative graph pattern mining

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00110952" target="_blank" >RIV/00216224:14330/19:00110952 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3331076.3331113" target="_blank" >http://dx.doi.org/10.1145/3331076.3331113</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3331076.3331113" target="_blank" >10.1145/3331076.3331113</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A genetic algorithm for discriminative graph pattern mining

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A genetic algorithm for discriminative graph pattern mining

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 23rd International Database Applications & Engineering Symposium, IDEAS 2019, Athens, Greece

ISBN

9781450362498

ISSN

—

e-ISSN

—

Počet stran výsledku

2

Strana od-do

461-462

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Athens

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—