Modeling Temporal Evolution and Multiscale Structure in Networks

Tue Herlau, Morten Mørup, Mikkel Schmidt
; Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):960-968, 2013.

Abstract

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v28-herlau13, title = {Modeling Temporal Evolution and Multiscale Structure in Networks}, author = {Tue Herlau and Morten Mørup and Mikkel Schmidt}, pages = {960--968}, year = {2013}, editor = {Sanjoy Dasgupta and David McAllester}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/herlau13.pdf}, url = {http://proceedings.mlr.press/v28/herlau13.html}, abstract = {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. } }
Endnote
%0 Conference Paper %T Modeling Temporal Evolution and Multiscale Structure in Networks %A Tue Herlau %A Morten Mørup %A Mikkel Schmidt %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-herlau13 %I PMLR %J Proceedings of Machine Learning Research %P 960--968 %U http://proceedings.mlr.press %V 28 %N 3 %W PMLR %X 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.
RIS
TY - CPAPER TI - Modeling Temporal Evolution and Multiscale Structure in Networks AU - Tue Herlau AU - Morten Mørup AU - Mikkel Schmidt BT - Proceedings of the 30th International Conference on Machine Learning PY - 2013/02/13 DA - 2013/02/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-herlau13 PB - PMLR SP - 960 DP - PMLR EP - 968 L1 - http://proceedings.mlr.press/v28/herlau13.pdf UR - http://proceedings.mlr.press/v28/herlau13.html AB - 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. ER -
APA
Herlau, T., Mørup, M. & Schmidt, M.. (2013). Modeling Temporal Evolution and Multiscale Structure in Networks. Proceedings of the 30th International Conference on Machine Learning, in PMLR 28(3):960-968

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