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@inproceedings{1423306,
author = {Vaculík, Karel and Popelínský, Lubomír},
address = {NY, USA},
booktitle = {IDEAS '18 Proceedings of the 22nd International Database Engineering & Applications Symposium},
doi = {http://dx.doi.org/10.1145/3216122.3216172},
keywords = {data mining;discriminative patterns;dynamic graphs;graph mining;pattern mining;random walk},
howpublished = {elektronická verze "online"},
language = {eng},
location = {NY, USA},
isbn = {978-1-4503-6527-7},
pages = {95-102},
publisher = {ACM New York},
title = {WalDis: Mining Discriminative Patterns within Dynamic Graphs},
url = {https://dl.acm.org/citation.cfm?id=3216172},
year = {2018}
}
TY - JOUR
ID - 1423306
AU - Vaculík, Karel - Popelínský, Lubomír
PY - 2018
TI - WalDis: Mining Discriminative Patterns within Dynamic Graphs
PB - ACM New York
CY - NY, USA
SN - 9781450365277
KW - data mining;discriminative patterns;dynamic graphs;graph mining;pattern mining;random walk
UR - https://dl.acm.org/citation.cfm?id=3216172
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 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.
ER -
VACULÍK, Karel a Lubomír POPELÍNSKÝ. WalDis: Mining Discriminative Patterns within Dynamic Graphs. Online. In \textit{IDEAS '18 Proceedings of the 22nd International Database Engineering \&{} Applications Symposium}. NY, USA: ACM New York, 2018, s.~95-102. ISBN~978-1-4503-6527-7. Dostupné z: https://dx.doi.org/10.1145/3216122.3216172.
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