Temporal stability of network partitions

TL;DR

This paper introduces a method for identifying and ranking stable network partitions over time, capturing persistent structures and significant events in temporal networks using coevolving random walkers.

Contribution

It generalizes partition stability to temporal networks, enabling the detection of meaningful persistent structures and isolated events across different time-scales.

Findings

Uncovers structures persistent over meaningful time-scales

Detects important isolated events in networks

Effective for studying network evolution

Abstract

We present a method to find the best temporal partition at any time-scale and rank the relevance of partitions found at different time-scales. This method is based on random walkers coevolving with the network and as such constitutes a generalization of partition stability to the case of temporal networks. We show that, when applied to a toy model and real datasets, temporal stability uncovers structures that are persistent over meaningful time-scales as well as important isolated events, making it an effective tool to study both abrupt changes and gradual evolution of a network mesoscopic structures.