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@inproceedings{1569319, author = {Vaculík, Karel and Popelínský, Lubomír}, address = {New York}, booktitle = {Proceedings of the 23rd International Database Applications & Engineering Symposium, IDEAS 2019, Athens, Greece}, doi = {http://dx.doi.org/10.1145/3331076.3331113}, editor = {Bipin C. Desai and Dimosthenis Anagnostopoulos and Yannis Manolopoulos and Mara Nikolaidou}, keywords = {data mining; graph mining; dynamic graphs; pattern mining; discriminative patterns; random walk; genetic algorithm}, howpublished = {elektronická verze "online"}, language = {eng}, location = {New York}, isbn = {978-1-4503-6249-8}, pages = {461-462}, publisher = {ACM}, title = {A genetic algorithm for discriminative graph pattern mining}, year = {2019} }
TY - JOUR ID - 1569319 AU - Vaculík, Karel - Popelínský, Lubomír PY - 2019 TI - A genetic algorithm for discriminative graph pattern mining PB - ACM CY - New York SN - 9781450362498 KW - data mining KW - graph mining KW - dynamic graphs KW - pattern mining KW - discriminative patterns KW - random walk KW - genetic algorithm N2 - 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. ER -
VACULÍK, Karel a Lubomír POPELÍNSKÝ. A genetic algorithm for discriminative graph pattern mining. Online. In Bipin C. Desai and Dimosthenis Anagnostopoulos and Yannis Manolopoulos and Mara Nikolaidou. \textit{Proceedings of the 23rd International Database Applications \&{} Engineering Symposium, IDEAS 2019, Athens, Greece}. New York: ACM, 2019, s.~461-462. ISBN~978-1-4503-6249-8. Dostupné z: https://dx.doi.org/10.1145/3331076.3331113.
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