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- W4284989076 abstract "In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is a promising algorithm to identify overlapping symptoms but its performance in psychological networks has not been evaluated. In this study, we compared the choice of the CP model parameters based on a metric named fuzzy modularity (CPMod) with two other metrics, the ratio of the two large communities (CPRat) and entropy (CPEnt). We evaluated the performance of the CP algorithm to: (1) identify the correct number of latent factors (i.e. communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e. overlapping symptoms). We carried out simulations under 108 conditions (2x2x3x3x3): (1) number of factors (2 and 4); (2) number of observed variables per factor (4 and 8); (3) factor correlations (0.0, 0.5, and 0.7); (4) proportion of observed variables with cross-loadings (0.0%, 12.5% and 25.0%); and (5) sample size (300, 500 and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), Sensitivity, Specificity and mean number of isolated nodes. We also compared the CP with two established methods to investigate dimensionality, Exploratory Factor Analysis and the Walktrap algorithm (both modified to consider overlap). The CPMod had the best performance across almost all conditions and is the currently recommended option to identify communities with overlapping symptoms instead of the CPRat and CPEnt." @default.
- W4284989076 created "2022-07-11" @default.
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- W4284989076 date "2022-07-06" @default.
- W4284989076 modified "2023-09-27" @default.
- W4284989076 title "The performance of the Clique Percolation to identify overlapping symptoms in psychological networks" @default.
- W4284989076 doi "https://doi.org/10.31234/osf.io/fk963" @default.
- W4284989076 hasPublicationYear "2022" @default.
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