Detailed Information on Publication Record
2017
WalDis: Mining Discriminative Patterns within Dynamic Graphs
VACULÍK, Karel and Lubomír POPELÍNSKÝBasic information
Original name
WalDis: Mining Discriminative Patterns within Dynamic Graphs
Authors
VACULÍK, Karel (203 Czech Republic, guarantor, belonging to the institution) and Lubomír POPELÍNSKÝ (203 Czech Republic, belonging to the institution)
Edition
MEMICS 2017. 12th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science, 2017
Other information
Language
English
Type of outcome
Konferenční abstrakt
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Czech Republic
Confidentiality degree
není předmětem státního či obchodního tajemství
RIV identification code
RIV/00216224:14330/17:00099794
Organization unit
Faculty of Informatics
Keywords (in Czech)
dolování z dat; dolování z grafů; dynamické grafy; dolování vzorů; diskriminativní vzory; náhodná procházka
Keywords in English
data mining; graph mining; dynamic graphs; pattern mining; discriminative patterns; random walk
Tags
International impact, Reviewed
Změněno: 11/4/2018 12:10, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
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.
Links
| MUNI/A/0897/2016, interní kód MU |
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