Temporal effects in the growth of networks

TL;DR

This paper introduces a model combining node fitness heterogeneity and decay to better explain the growth of citation networks, showing diverse degree distributions that align with real-world data.

Contribution

The paper presents a simple, analytically solvable model integrating both heterogeneity and decay in node fitness for network growth.

Findings

Degree distribution can be exponential, log-normal, or power-law.

Model aligns with various real-world network data.

Combines previous isolated assumptions into a unified framework.

Abstract

We show that to explain the growth of the citation network by preferential attachment (PA), one has to accept that individual nodes exhibit heterogeneous fitness values that decay with time. While previous PA-based models assumed either heterogeneity or decay in isolation, we propose a simple analytically treatable model that combines these two factors. Depending on the input assumptions, the resulting degree distribution shows an exponential, log-normal or power-law decay, which makes the model an apt candidate for modeling a wide range of real systems.