WalDis: Mining Discriminative Patterns within Dynamic Graphs

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00099794" target="_blank" >RIV/00216224:14330/17:00099794 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

WalDis: Mining Discriminative Patterns within Dynamic Graphs

Original language description

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 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.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

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

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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