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

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.

A Tiny Viral Protein, SARS-CoV-2-ORF7b: Functional Molecular Mechanisms

“…Figure S1 S1: List of 75 proteins of level 6 to 4 present in BioGRID as specific interactors of ORF7b; Table S2: List of 51 proteins extracted from BioGRID and used on STRING to calculate the human interactome model. References [13,90,[182][183][184][185][186][187][188][189][190][191][192][193][194][195][196] are cited in the supplementary materials. Funding: This research was funded by a grant to Giovanna Fusco, Ministero della Salute, number C73C18000160005 (invoice addressed to Giovanna Fusco, Istituto Zooprofilattico Sperimentale del Mezzogiorno, via Salute, 2-80055 Portici (Napoli), Italy).…”

An agent-based model of the spread of behavioural risk-factors for cardiovascular disease in city-scale populations

“…SBMs have previously been used to model real-world networks, particularly with respect to degree-distribution, but at relatively high computational cost [58]. It is also worth noting that SBMs do not fully account for the scale-free aspects of real-world networks [58], and have only been applied to relatively small networks (around 10K individuals [59] where calibration data exists [60,61], rather than up to 1.2M in our ABM).…”