Modeling Temporal Evolution and Multiscale Structure in Networks

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

189 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the 30 th International Conference on Machine Learning
Publication date2013
Publication statusPublished - 2013
Event30th International Conference on Machine Learning (ICML 2013) - Atlanta, Georgia, United States
Duration: 16 Jun 201321 Jun 2013


Conference30th International Conference on Machine Learning (ICML 2013)
Country/TerritoryUnited States
CityAtlanta, Georgia
Internet address
SeriesJMLR: Workshop and Conference Proceedings
NumberCycle 3


Dive into the research topics of 'Modeling Temporal Evolution and Multiscale Structure in Networks'. Together they form a unique fingerprint.

Cite this