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@proceedings{1406106,
author = {Vaculík, Karel and Popelínský, Lubomír},
booktitle = {MEMICS 2017. 12th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science},
keywords = {data mining; graph mining; dynamic graphs; pattern mining; discriminative patterns; random walk},
language = {eng},
title = {WalDis: Mining Discriminative Patterns within Dynamic Graphs},
year = {2017}
}
TY - CONF
ID - 1406106
AU - Vaculík, Karel - Popelínský, Lubomír
PY - 2017
TI - WalDis: Mining Discriminative Patterns within Dynamic Graphs
KW - data mining
KW - graph mining
KW - dynamic graphs
KW - pattern mining
KW - discriminative patterns
KW - random walk
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 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. In \textit{MEMICS 2017. 12th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science}. 2017.
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