Coloured graphlet profiles as a predictor of career length in scientific co-authorship networks

Oliver Blanthorn; Eva Navarro Lopez

Coloured graphlet profiles as a predictor of career length in scientific co-authorship networks

Formal Methods

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

Graphlets, or induced motifs, have long been used to find important medium-scale structures in directed networks. We present a method using the composition of coloured graphlets in ego-networks to characterise nodes. We give an example application using our technique to predict the numbers of years researchers are active from their collaboration networks, and compare our success with simpler metrics; particularly, we find that the use of coloured graphlets improves predictive
performance compared to colour-blind graphlets; that 4-star graphlets centred on an author are predictors of a long career, and that this effect is not degenerate to centralities.

Original language	English
Title of host publication	4th World Conference on Complex Systems: Emergence, Self-organization, Nonlinear Dynamics and Complexity
Publisher	IEEE
Pages	1
Number of pages	6
Publication status	Accepted/In press - 14 Jan 2019

Keywords

Graphlets
Motifs
Complex networks
Co-authorship
Science of science
Collaboration
Career lenght
Ego-networks

Embargoed Document

blanthorn-navarro_last_version
Accepted author manuscript, 349 KB
Embargo ends: 1/01/29

Cite this

@inproceedings{5f0a4e194c5b400da7819034ff60a3c2,

title = "Coloured graphlet profiles as a predictor of career length in scientific co-authorship networks",

abstract = "Graphlets, or induced motifs, have long been used to find important medium-scale structures in directed networks. We present a method using the composition of coloured graphlets in ego-networks to characterise nodes. We give an example application using our technique to predict the numbers of years researchers are active from their collaboration networks, and compare our success with simpler metrics; particularly, we find that the use of coloured graphlets improves predictiveperformance compared to colour-blind graphlets; that 4-star graphlets centred on an author are predictors of a long career, and that this effect is not degenerate to centralities.",

keywords = "Graphlets, Motifs, Complex networks, Co-authorship, Science of science, Collaboration, Career lenght, Ego-networks",

author = "Oliver Blanthorn and {Navarro Lopez}, Eva",

note = "4th World Conference on Complex Systems: Emergence, Self-organization, Nonlinear Dynamics and Complexity, (Ouarzazate, Morocco, 22–25 April, 2019).",

year = "2019",

month = jan,

day = "14",

language = "English",

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booktitle = "4th World Conference on Complex Systems: Emergence, Self-organization, Nonlinear Dynamics and Complexity",

publisher = "IEEE",

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T1 - Coloured graphlet profiles as a predictor of career length in scientific co-authorship networks

AU - Blanthorn, Oliver

AU - Navarro Lopez, Eva

N1 - 4th World Conference on Complex Systems: Emergence, Self-organization, Nonlinear Dynamics and Complexity, (Ouarzazate, Morocco, 22–25 April, 2019).

PY - 2019/1/14

Y1 - 2019/1/14

N2 - Graphlets, or induced motifs, have long been used to find important medium-scale structures in directed networks. We present a method using the composition of coloured graphlets in ego-networks to characterise nodes. We give an example application using our technique to predict the numbers of years researchers are active from their collaboration networks, and compare our success with simpler metrics; particularly, we find that the use of coloured graphlets improves predictiveperformance compared to colour-blind graphlets; that 4-star graphlets centred on an author are predictors of a long career, and that this effect is not degenerate to centralities.

AB - Graphlets, or induced motifs, have long been used to find important medium-scale structures in directed networks. We present a method using the composition of coloured graphlets in ego-networks to characterise nodes. We give an example application using our technique to predict the numbers of years researchers are active from their collaboration networks, and compare our success with simpler metrics; particularly, we find that the use of coloured graphlets improves predictiveperformance compared to colour-blind graphlets; that 4-star graphlets centred on an author are predictors of a long career, and that this effect is not degenerate to centralities.

KW - Graphlets

KW - Motifs

KW - Complex networks

KW - Co-authorship

KW - Science of science

KW - Collaboration

KW - Career lenght

KW - Ego-networks

M3 - Conference contribution

SP - 1

BT - 4th World Conference on Complex Systems: Emergence, Self-organization, Nonlinear Dynamics and Complexity

PB - IEEE

ER -

Coloured graphlet profiles as a predictor of career length in scientific co-authorship networks

Abstract

Keywords

Embargoed Document

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