We propose a random network model incorporating heterogeneity of agents and a continuous notion of homophily. Unlike … the vast majority of the corresponding economic literature, we capture homophily in terms of similarity rather than … equality of agents. We show that if links between similar agents are indeed more likely, our homophilous random network model …

payoffs depend on the social distance between them. We characterize the clustering coefficient and average path length in … distributions, positive assortativity of degrees, and clustering-degree correlation. …

We propose a random network model incorporating heterogeneity of agents and a continuous notion of homophily. Unlike … positive level of homophily our model exhibits clustering, that is an increased probability of linkage given a common neighbor … the vast majority of the corresponding economic literature, we capture homophily in terms of similarity rather than …

We propose a random network model incorporating heterogeneity of agents and a continuous notion of homophily. Unlike … the vast majority of the corresponding economic literature, we capture homophily in terms of similarity rather than … equality of agents. We show that if links between similar agents are indeed more likely, our homophilous random network model …

third partners. We propose a micro perspective to understand how endogenous network effects, cognitive proximity of actors … and their interplay influence the evolutionary process of network formation in clusters. …

This paper aims to further our emerging knowledge on the external linkages of regional clusters. We adopt a network …

that amount. The information that the nodes receive through the network is the revenue from investments in links. We prove … maximizing the aggregate profit, are still the all-encompassing star and the complete network, with the sole and rare exception … of a highly particular case where there is a draw between the allencompassing star, the complete network and a whole …

The communities in a network have distinct characteristics and interrelationships. Community discovery methods based on … node embedding and deep learning have surpassed spectral clustering and statistical inference as the preferred methods for … handling high-dimensional network data. Community detection in graph networks using graph neural networks (GNNs) is a rapidly …

methodological tools assessing network characteristics at the dyadic level are lacking for aggregated networks (such as the network … knowledge flows. To do so we first define a new measure to assess 'network proximity' at the level of the regional dyad, based … European countries (France, Germany, Italy, Spain, the United Kingdom). The studied network is then composed of all the …