Homophily and Triadic Closure in Evolving Social Networks
arXiv:1504.07101
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.
32 pages, 8 figures, 4 tables, submitted. Exposition of the work improved. Title upadated to : Modeling Networks with a Growing Feature-Structure
References in corpus (17)
- Emergence of scaling in random networks
- Statistical mechanics of complex networks
- The structure and function of complex networks
- Mixing patterns in networks
- Quantifying social group evolution
- Dynamical and correlation properties of the Internet
- Growing Scale-Free Networks with Tunable Clustering
- Science vs Conspiracy: collective narratives in the age of (mis)information
- Growing networks with local rules: preferential attachment, clustering hierarchy and degree correlations
- A Separable Model for Dynamic Networks
- Triadic closure as a basic generating mechanism of communities in complex networks
- A Model for Social Networks
- Duplication-divergence model of protein interaction network
- Assortative mixing in Protein Contact Networks and protein folding kinetics
- Nonparametric Link Prediction in Dynamic Networks
- An Infinite Latent Attribute Model for Network Data
- Central limit theorems for an Indian buffet model with random weights