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Main Authors: Liu, Cathy Xuanchi, Alexander, Tristram J., Altmann, Eduardo G.
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2405.14168
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author Liu, Cathy Xuanchi
Alexander, Tristram J.
Altmann, Eduardo G.
author_facet Liu, Cathy Xuanchi
Alexander, Tristram J.
Altmann, Eduardo G.
contents Large complex networks are often organized into groups or communities. In this paper, we introduce and investigate a generative model of network evolution that reproduces all four pairwise community types that exist in directed networks: assortative, core-periphery, disassortative, and the newly introduced source-basin type. We fix the number of nodes and the community membership of each node, allowing node connectivity to change through rewiring mechanisms that depend on the community membership of the involved nodes. We determine the dependence of the community relationship on the model parameters using a mean-field solution. It reveals that a difference in the swap probabilities of the two communities is a necessary condition to obtain a core-periphery relationship and that a difference in the average in-degree of the communities is a necessary condition for a source-basin relationship. More generally, our analysis reveals multiple possible scenarios for the transition between the different structure types, and sheds light on the mechanisms underlying the observation of the different types of communities in network data.
format Preprint
id arxiv_https___arxiv_org_abs_2405_14168
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A generative model for community types in directed networks
Liu, Cathy Xuanchi
Alexander, Tristram J.
Altmann, Eduardo G.
Social and Information Networks
Physics and Society
Large complex networks are often organized into groups or communities. In this paper, we introduce and investigate a generative model of network evolution that reproduces all four pairwise community types that exist in directed networks: assortative, core-periphery, disassortative, and the newly introduced source-basin type. We fix the number of nodes and the community membership of each node, allowing node connectivity to change through rewiring mechanisms that depend on the community membership of the involved nodes. We determine the dependence of the community relationship on the model parameters using a mean-field solution. It reveals that a difference in the swap probabilities of the two communities is a necessary condition to obtain a core-periphery relationship and that a difference in the average in-degree of the communities is a necessary condition for a source-basin relationship. More generally, our analysis reveals multiple possible scenarios for the transition between the different structure types, and sheds light on the mechanisms underlying the observation of the different types of communities in network data.
title A generative model for community types in directed networks
topic Social and Information Networks
Physics and Society
url https://arxiv.org/abs/2405.14168