Abstract
This paper explores the social determinants of health through a network science based approach to analyzing the Latino MSM Community Involvement (LMSM-CI) dataset. Data are clustered to determine identifying characteristics of groups of participants in 3 categories: high self esteem, susceptibility to alcohol abuse, and HIV positive status. A question arises as to the best methodology for inferring a graph from the data, as well as for clustering and analyzing the network. To that end we use 4 different graph inference methods: inverse covariance selection (Glasso), neighborhood selection (MB), Sparse Correlations for Compositional data (SparCC) and the traditional k-Nearest Neighbors (kNN). For each inference we test 4 different clustering methods: Louvain, Leiden, NBR-Clust with VAT, and NBR-Clust with integrity. Surprisingly, the Glasso and MB inference methods produce better clusterings than kNN, as determined by a suite of internal evaluation measures. The most promising clusterings are visualized and their properties are analyzed.
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Sanjel, P., Matta, J. (2022). Inferred Networks and the Social Determinants of Health. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_58
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DOI: https://doi.org/10.1007/978-3-030-93413-2_58
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