Learning patterns in the dynamics of biological networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Speedup for a periodic subgraph miner
Information Processing Letters
Community Discovery via Metagraph Factorization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Discovering the Evolutionary Patterns in Local Topology of an E-Mail Social Network
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Mining periodic behaviors of object movements for animal and biological sustainability studies
Data Mining and Knowledge Discovery
Towards group behavioral reason mining
Expert Systems with Applications: An International Journal
A query based approach for mining evolving graphs
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Mining frequent correlated graphs with a new measure
Expert Systems with Applications: An International Journal
Mining most frequently changing component in evolving graphs
World Wide Web
Discovering descriptive rules in relational dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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Social interactions that occur regularly typically correspond to significant yet often infrequent and hard to detect interaction patterns. To identify such regular behavior, we propose a new mining problem of finding periodic or near periodic subgraphs in dynamic social networks. We analyze the computational complexity of theproblem, showing that, unlike any of the related subgraph mining problems, it is polynomial. We propose a practical, efficient and scalable algorithm to find such subgraphs that takes imperfect periodicity into account. We demonstrate the applicability of our approach on severalreal-world networks and extract meaningful and interesting periodic interaction patterns.