Coloured graphlet profiles as a predictor of career length in scientific co-authorship networks
AbstractGraphlets, 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.
CitationBlanthorn, O. and Navarro-Lopez, E. (2019) Coloured graphlet profiles as a predictor of career length in scientific co-authorship networks. 2019 4th World Conference on Complex Systems (WCCS), 22-25 April 2019, Ouarzazate, Morocco.
DescriptionThis is an accepted manuscript of an article published by IEEE in 2019 4th World Conference on Complex Systems (WCCS), available online: https://ieeexplore.ieee.org/document/8930762 The accepted version of the publication may differ from the final published version.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/