Random walk based snapshot clustering for detecting community dynamics in temporal networks

TL;DR

This paper presents a novel random walk-based method for detecting stable community structures and structural shifts in temporal networks, providing a low-dimensional snapshot representation and validated through synthetic and real-world datasets.

Contribution

Introduces a new random walk-based approach for snapshot clustering in temporal networks, enabling detection of community dynamics and structural shifts with effective benchmarking.

Findings

Successfully detects community splits, merges, births, and deaths.

Provides a low-dimensional feature space for snapshot comparison.

Outperforms several state-of-the-art algorithms in tests.

Abstract

The evolution of many dynamical systems that describe relationships or interactions between objects can be effectively modeled by temporal networks, which are typically represented as a sequence of static network snapshots. In this paper, we introduce a novel random walk-based approach that can identify clusters of time-snapshots in which network community structures are stable. This allows us to detect significant structural shifts over time, such as the splitting or merging of communities or their births and deaths. We also provide a low-dimensional representation of entire snapshots, placing those with similar community structure close to each other in the feature space. To validate our approach, we develop an agent-based algorithm that generates synthetic datasets with the desired characteristic properties, enabling thorough testing and benchmarking. We further demonstrate the effectiveness and broad applicability of our technique by testing it on various social dynamics models and real-world datasets and comparing its performance to several state-of-the-art algorithms. Our findings highlight the strength of our approach to correctly capture and analyze the dynamics of complex systems.