WalDis: Mining Discriminative Patterns within Dynamic Graphs

This publication doesn't include Faculty of Arts. It includes Faculty of Informatics. Official publication website can be found on muni.cz.



Type Article in Proceedings
Conference IDEAS '18 Proceedings of the 22nd International Database Engineering & Applications Symposium
MU Faculty or unit

Faculty of Informatics

Web https://dl.acm.org/citation.cfm?id=3216172
Doi http://dx.doi.org/10.1145/3216122.3216172
Keywords data mining;discriminative patterns;dynamic graphs;graph mining;pattern mining;random walk
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 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.
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