Hubs, authorities, and communities
ACM Computing Surveys (CSUR)
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Communication motifs: a tool to characterize social communications
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Statistical Analysis and Data Mining
Stochastic Network Motif Detection in Social Media
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Community detection in Social Media
Data Mining and Knowledge Discovery
Detecting multiple stochastic network motifs in network data
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Mining structural hole spanners through information diffusion in social networks
Proceedings of the 22nd international conference on World Wide Web
| Hi-index | 0.00 |
Many real-world problems exhibit phenomena which are best represented as complex networks with dynamic structures (e.g., human communication networks). Network motifs have been shown effective for characterizing the structural properties of such complex networks. Nevertheless, related motif models typically do not consider stochastic structural and sequential variations, hinting their limitations on dynamic network analysis. In this paper, we consider networks with time-stamped edges and model their local structural and temporal variations using a mixture of Markov chains for stochastic temporal network motif detection. The optimal number of motifs is automatically estimated in a Bayesian framework. We evaluated the proposed method using synthetic networks and found to be robust against noise compared to the deterministic approach. Also, we applied it to a mobile phone usage data set to demonstrate how the human communication patterns embedded in the data set can be detected. In addition, we make use of a hidden Markov model with different distributions for the mixing proportions of the motifs defining its states, and demonstrated how the evolution of the communication patterns can also be identified.