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Data from: The role of social attraction and social avoidance in shaping modular networks

Cite this dataset

Romano, Valéria; Puga-Gonzalez, Ivan; MacIntosh, Andrew; Sueur, Cédric (2024). Data from: The role of social attraction and social avoidance in shaping modular networks [Dataset]. Dryad. https://doi.org/10.5061/dryad.7wm37pw0x

Abstract

In this study, we simulated the emergence of networks depending on simple rules of social attraction (e.g. access to the highest benefits) and social avoidance (e.g. avoiding the highest costs). Individuals vary in the degree to which they signal benefits versus costs, and the formation of relationships depends on the extent to which these benefits and/or costs are perceived. We used matrices containing 2000 interactions per individual to estimate network properties. 

README: The role of social attraction and social avoidance in shaping modular networks

https://doi.org/10.5061/dryad.7wm37pw0x

These datasets comprise 20 simulations for each of the 20 conditions tested. Each simulation comprises 2000-time steps in the Optimal Relationship Model.

Description of the data and file structure

Datasets included:

  1. IndNetworkMetrics_ORP:
    Distribution of benefits and costs per individual and across the 20 conditions of study. This data was used to
    estimate the Gini coefficients.
    Individual network metrics (eigenvector, betweenness and strength).

  2. GlobalNetworkMetrics_ORP:
    Values of density, modularity, and centralization per condition and different group sizes.

    Missing values are denoted by NA.

Code/Software

The Optimal Relationship Model was written in Netlogo v. 6.0. A detailed description of the model according to the ODD (Overview, Design concepts, and Details) Protocol and the source codes of the model are given in the Supplementary Material of our manuscript.

For each time step, we recorded the identities of the interacting individuals as well as the updated values of their relationship weights. We then used the matrices containing 2000 interactions per individual in our analyses. This dataset gave us the resulting network properties found under each of the conditions. This is the data available here.

Funding

Coordenação de Aperfeicoamento de Pessoal de Nível Superior, Award: 1216/13-9

National Council of Science and Technology (CONACyT)

University of Strasbourg, Institute for Advanced Studies

Japan Society for the Promotion of Science, Award: P17803

Japan Society for the Promotion of Science, Award: 24770232

Japan Society for the Promotion of Science, Award: 16H06181