Interdisciplinary Information Sciences
Online ISSN : 1347-6157
Print ISSN : 1340-9050
ISSN-L : 1340-9050
Modeling Networks with a Growing Feature-Structure
Irene CRIMALDIMichela DEL VICARIOGreg MORRISONWalter QUATTROCIOCCHIMassimo RICCABONI
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JOURNALS FREE ACCESS Advance online publication

Article ID: 2017.R.01

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Abstract

We present a new network model accounting for multidimensional assortativity. Each node is characterized by a number of features and the probability of a link between two nodes depends on common features. We do not fix a priori the total number of possible features. The bipartite network of the nodes and the features evolves according to a stochastic dynamics that depends on three parameters that respectively regulate the preferential attachment in the transmission of the features to the nodes, the number of new features per node, and the power-law behavior of the total number of observed features. Our model also takes into account a mechanism of triadic closure. We provide theoretical results and statistical estimators for the parameters of the model. We validate our approach by means of simulations and an empirical analysis of a network of scientific collaborations.

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© 2017 by the Graduate School of Information Sciences (GSIS), Tohoku University
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