The Fitness-Corrected Block Model, or how to create maximum-entropy data-driven spatial social networks

TL;DR

This paper introduces the Fitness-Corrected Block Model, a maximum-entropy, data-driven network model that captures spatial, demographic, and social features, useful for generating realistic synthetic social networks.

Contribution

It presents a novel, adjustable-density, maximum-entropy model extending the Degree-Corrected Block Model, with analytical degree distribution and spatial-social network applications.

Findings

Analytical degree distribution depends on constraints and fitness distribution.

Model accurately reproduces spatial and demographic features in synthetic networks.

Simulations confirm the model's effectiveness in urban social network scenarios.

Abstract

Models of networks play a major role in explaining and reproducing empirically observed patterns. Suitable models can be used to randomize an observed network while preserving some of its features, or to generate synthetic graphs whose properties may be tuned upon the characteristics of a given population. In the present paper, we introduce the Fitness-Corrected Block Model, an adjustable-density variation of the well-known Degree-Corrected Block Model, and we show that the proposed construction yields a maximum entropy model. When the network is sparse, we derive an analytical expression for the degree distribution of the model that depends on just the constraints and the chosen fitness-distribution. Our model is perfectly suited to define maximum-entropy data-driven spatial social networks, where each block identifies vertices having similar position (e.g., residence) and age, and where the expected block-to-block adjacency matrix can be inferred from the available data. In this case, the sparse-regime approximation coincides with a phenomenological model where the probability of a link binding two individuals is directly proportional to their sociability and to the typical cohesion of their age-groups, whereas it decays as an inverse-power of their geographic distance. We support our analytical findings through simulations of a stylized urban area.