Arrow Research search
Back to ICML

ICML 2013

Modeling Temporal Evolution and Multiscale Structure in Networks

Conference Paper Cycle 3 Papers Artificial Intelligence · Machine Learning

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.

Authors

Keywords

No keywords are indexed for this paper.

Context

Venue
International Conference on Machine Learning
Archive span
1993-2025
Indexed papers
16471
Paper id
301332282630445809
v2026.09.13