Imperial College London
Alternate

DrTimEvans

Faculty of Natural Sciences, Department of Physics

Senior Lecturer
 
 
 
//

Contact

 

+44 (0)20 7594 7837t.evans Website

 
 
//

Assistant

 

Mrs Graziela De Nadai-Sowrey +44 (0)20 7594 7843

 
//

Location

 

609Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Falkenberg:2020:10.1103/physrevresearch.2.023352,
author = {Falkenberg, M and Lee, J-H and Amano, S-I and Ogawa, K-I and Yano, K and Miyake, Y and Evans, TS and Christensen, K},
doi = {10.1103/physrevresearch.2.023352},
journal = {Physical Review & Research International},
pages = {023352  1--023352  17},
title = {Identifying time dependence in network growth},
url = {http://dx.doi.org/10.1103/physrevresearch.2.023352},
volume = {2},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Identifying power-law scaling in real networks—indicative of preferential attachment—has proved controversial. Critics argue that measuring the temporal evolution of a network directly is better than measuring the degree distribution when looking for preferential attachment. However, many of the established methods do not account for any potential time dependence in the attachment kernels of growing networks, or methods assume that node degree is the key observable determining network evolution. In this paper, we argue that these assumptions may lead to misleading conclusions about the evolution of growing networks. We illustrate this by introducing a simple adaptation of the Barabási-Albert model, the “k2 model,” where new nodes attach to nodes in the existing network in proportion to the number of nodes one or two steps from the target node. The k2 model results in time dependent degree distributions and attachment kernels, despite initially appearing to grow as linear preferential attachment, and without the need to include explicit time dependence in key network parameters (such as the average out-degree). We show that similar effects are seen in several real world networks where constant network growth rules do not describe their evolution. This implies that measurements of specific degree distributions in real networks are likely to change over time.
AU  - Falkenberg,M
AU  - Lee,J-H
AU  - Amano,S-I
AU  - Ogawa,K-I
AU  - Yano,K
AU  - Miyake,Y
AU  - Evans,TS
AU  - Christensen,K
DO  - 10.1103/physrevresearch.2.023352
EP  - 1
PY  - 2020///
SN  - 2231-1815
SP  - 023352
TI  - Identifying time dependence in network growth
T2  - Physical Review & Research International
UR  - http://dx.doi.org/10.1103/physrevresearch.2.023352
UR  - https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.023352
UR  - http://hdl.handle.net/10044/1/80428
VL  - 2
ER  -
Download