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The quest for a model that is able to explain, describe, analyze and simulatereal-world complex networks is of uttermost practical as well as theoreticalinterest. In this paper we introduce and study a network model that is based ona latent attribute structure: each node is characterized by a number offeatures and the probability of the existence of an edge between two nodesdepends on the features they share. Features are chosen according to a processof Indian-Buffet type but with an additional random "fitness" parameterattached to each node, that determines its ability to transmit its own featuresto other nodes. As a consequence, a node's connectivity does not depend on itsage alone, so also "young" nodes are able to compete and succeed in acquiringlinks. One of the advantages of our model for the latent bipartite"node-attribute" network is that it depends on few parameters with astraightforward interpretation. We provide some theoretical, as wellexperimental, results regarding the power-law behaviour of the model and theestimation of the parameters. By experimental data, we also show how theproposed model for the attribute structure naturally captures most local andglobal properties (e.g., degree distributions, connectivity and distancedistributions) real networks exhibit. keyword: Complex network, social network,attribute matrix, Indian Buffet process

A Network Model characterized by a Latent Attribute Structure with Competition