Higher order assortativity for directed weighted networks and Markov chains

Abstract

This paper proposes a new class of assortativity measures for weighted and directed networks. We extend the classical Newman's degree-degree assortativity by considering nodes' attributes different from the degree. Moreover, we propose connections among the nodes through directed paths of length greater than one, thus obtaining higher-order assortativity. We provide an empirical application of these measures for the paradigmatic case of the trade network. Importantly, we show how this global network indicator is strongly related to the autocorrelations of the states of a Markov chain.

References in corpus (16)

• Assortative mixing in networks
• Discrete-time Markov chain approach to contact-based disease spreading in complex networks
• Dynamics on higher-order networks: A review
• Structure and Evolution of the World Trade Network
• What are higher-order networks?
• Fitness-dependent topological properties of the World Trade Web
• On the Topological Properties of the World Trade Web: A Weighted Network Analysis
• Multinetwork of international trade: A commodity-specific analysis
• Edge direction and the structure of networks
• Patterns of dominant flows in the world trade web
• Weighted Assortative And Disassortative Networks Model
• Effective Distances for Epidemics Spreading on Complex Networks
• International Trade Network: Country centrality and COVID-19 pandemic
• Systemic risk assessment through high order clustering coefficient
• Higher order assortativity in complex networks
• The multilayer architecture of the global input-output network and its properties