WalDis: Mining Discriminative Patterns within Dynamic Graphs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00103236" target="_blank" >RIV/00216224:14330/18:00103236 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3216122.3216172" target="_blank" >http://dx.doi.org/10.1145/3216122.3216172</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3216122.3216172" target="_blank" >10.1145/3216122.3216172</a>

Jazyk výsledku

angličtina

Název v původním jazyce

WalDis: Mining Discriminative Patterns within Dynamic Graphs

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

WalDis: Mining Discriminative Patterns within Dynamic Graphs

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

IDEAS '18 Proceedings of the 22nd International Database Engineering & Applications Symposium

ISBN

9781450365277

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

95-102

Název nakladatele

ACM New York

Místo vydání

NY, USA

Místo konání akce

Villa San Giovanni, Italy

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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