Many real-world networks exhibit both temporal evolution and multiscale structure. We propose a model for temporally correlated multifurcating hierarchies in complex networks which jointly capture both effects. We use the Gibbs fragmentation tree as prior over multifurcating trees and a change-point model to account for the temporal evolution of each vertex. We demonstrate that our model is able to infer time-varying multiscale structure in synthetic as well as three real world time-evolving complex networks. Our modeling of the temporal evolution of hierarchies brings new insights into the changing roles and position of entities and possibilities for better understanding these dynamic complex systems.
Proceedings of the 30 Th International Conference on Machine Learning, 2013, p. 960-968
Main Research Area:
Jmlr: Workshop and Conference Proceedings
30th International Conference on Machine Learning (ICML 2013)International Conference on Machine Learning