945 research outputs found
Hypergraphx: a library for higher-order network analysis
From social to biological systems, many real-world systems are characterized
by higher-order, non-dyadic interactions. Such systems are conveniently
described by hypergraphs, where hyperedges encode interactions among an
arbitrary number of units. Here, we present an open-source python library,
hypergraphx (HGX), providing a comprehensive collection of algorithms and
functions for the analysis of higher-order networks. These include different
ways to convert data across distinct higher-order representations, a large
variety of measures of higher-order organization at the local and the
mesoscale, statistical filters to sparsify higher-order data, a wide array of
static and dynamic generative models, and an implementation of different
dynamical processes with higher-order interactions. Our computational framework
is general, and allows to analyse hypergraphs with weighted, directed, signed,
temporal and multiplex group interactions. We provide visual insights on
higher-order data through a variety of different visualization tools. We
accompany our code with an extended higher-order data repository, and
demonstrate the ability of HGX to analyse real-world systems through a
systematic analysis of a social network with higher-order interactions. The
library is conceived as an evolving, community-based effort, which will further
extend its functionalities over the years. Our software is available at
https://github.com/HGX-Team/hypergraph
Networks and the epidemiology of infectious disease
The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues
- …