ICML '06 Proceedings of the 23rd international conference on Machine learning
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
A generalized mean field algorithm for variational inference in exponential families
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Community evolution detection in dynamic heterogeneous information networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Role-dynamics: fast mining of large dynamic networks
Proceedings of the 21st international conference companion on World Wide Web
Intent-aware temporal query modeling for keyword suggestion
Proceedings of the 5th Ph.D. workshop on Information and knowledge
Topic model for analyzing purchase data with price information
Data Mining and Knowledge Discovery
Modeling dynamic behavior in large evolving graphs
Proceedings of the sixth ACM international conference on Web search and data mining
Human interaction discovery in smartphone proximity networks
Personal and Ubiquitous Computing
Modeling the dynamics of composite social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Online egocentric models for citation networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Roles in social networks: Methodologies and research issues
Web Intelligence and Agent Systems
| Hi-index | 0.00 |
In a dynamic social or biological environment, interactions between the underlying actors can undergo large and systematic changes. Each actor can assume multiple roles and their degrees of affiliation to these roles can also exhibit rich temporal phenomena. We propose a state space mixed membership stochastic blockmodel which can track across time the evolving roles of the actors. We also derive an efficient variational inference procedure for our model, and apply it to the Enron email networks, and rewiring gene regulatory networks of yeast. In both cases, our model reveals interesting dynamical roles of the actors.