Exploring and comparing temporal clustering methods
Social and Information Networks
2020-12-03 v1 Data Analysis, Statistics and Probability
Abstract
Description of temporal networks and detection of dynamic communities have been hot topics of research for the last decade. However, no consensual answers to these challenges have been found due to the complexity of the task. Static communities are not well defined objects, and adding a temporal dimension renders the description even more difficult. In this article, we propose a coherent temporal clustering method: the Best Combination of Local Communities (BCLC). Our method aims at finding a good balance between two conflicting objectives : closely following the short time evolution by finding optimal partitions at each time step and temporal smoothness, which privileges historical continuity.
Cite
@article{arxiv.2012.01287,
title = {Exploring and comparing temporal clustering methods},
author = {Jordan Cambe and Sebastian Grauwin and Patrick Flandrin and Pablo Jensen},
journal= {arXiv preprint arXiv:2012.01287},
year = {2020}
}