Higher order assortativity for directed weighted networks and Markov chains
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910351579152384 |
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| author | Arcagni, Alberto Cerqueti, Roy Grassi, Rosanna |
| author_facet | Arcagni, Alberto Cerqueti, Roy Grassi, Rosanna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_01737 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Higher order assortativity for directed weighted networks and Markov chains Arcagni, Alberto Cerqueti, Roy Grassi, Rosanna Physics and Society 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. |
| title | Higher order assortativity for directed weighted networks and Markov chains |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2304.01737 |