The Many Faces of Graph Dynamics
Yvonne Anne Pignolet, Matthieu Roy, Stefan Schmid, Gilles Tredan

TL;DR
This paper introduces centrality distance as a new metric to quantify and compare the evolution of complex networks over time, revealing structured patterns and differences across various network types.
Contribution
It proposes the centrality distance measure for analyzing graph dynamics and demonstrates its effectiveness across multiple models and real-world networks.
Findings
Centrality distances effectively characterize network evolution.
The approach distinguishes different network types and evolution speeds.
Results outperform null-model comparisons.
Abstract
The topological structure of complex networks has fascinated researchers for several decades, resulting in the discovery of many universal properties and reoccurring characteristics of different kinds of networks. However, much less is known today about the network dynamics: indeed, complex networks in reality are not static, but rather dynamically evolve over time. Our paper is motivated by the empirical observation that network evolution patterns seem far from random, but exhibit structure. Moreover, the specific patterns appear to depend on the network type, contradicting the existence of a "one fits it all" model. However, we still lack observables to quantify these intuitions, as well as metrics to compare graph evolutions. Such observables and metrics are needed for extrapolating or predicting evolutions, as well as for interpolating graph evolutions. To explore the many faces…
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Taxonomy
TopicsComplex Network Analysis Techniques · Evolutionary Game Theory and Cooperation · Opinion Dynamics and Social Influence
