Network dataset and variable distributions.

(XLSX)</p

Rank order of scaled feature importance scores across five factor predictive models.

Rank order of scaled feature importance scores across five factor predictive models.</p

Comparative performance of disparate machine learning algorithms.

(DOCX)</p

Overall, in-fold cross-validated performance across five factor predictive models.

Overall, in-fold cross-validated performance across five factor predictive models.</p

Distribution of predictive performance across individuals for each five factor-based outcome model.

(A) extraversion models; (B) agreeableness models; (C) conscientiousness models; (D) stability models; (E) openness to experience models. Reported R2 values represent the average R2 across all LOSO fold performances (N = 54) for each respective model.</p

Plots of idiographic model predictions.

(XLSX)</p

Extracted network features: Scope, definitions, and contextual meanings.

Extracted network features: Scope, definitions, and contextual meanings.</p

Overview of analysis pipeline.

(1A) Derivation of outcome data for modeling. Raw EMA data is separated into six weeks and the RMSSD for each personality state self-report response within each week is calculated for each participant. (1B-1) Weekly cross-sectional social networks are constructed from the raw infrared passive sensing device log data. (1B-2) Features from the constructed networks are calculated (9 global structural, 3 global nodal, and 13 egocentric network features) to serve as predictors for the machine learning models. (2) The machine learning modeling framework is parallelized to independently predict each of the five personality states’ RMSSD values. The model is trained on N-1 participants’ network feature and outcome data and validated on a held-out participant’s data. This is repeated 54 times such that each participant is held-out and trained using all other participants’ data (LOSO cross-validation). A uniquely tuned model is validated for each fold and each model’s predictions are saved. (3A) Individual model (fold) performance is assessed using variance explained (R2), and average R2 is calculated to assess overall performance of the machine learning framework across participants for each personality state outcome. (3B) Introspection of the models is performed via quantification of feature importance.</p

R script to perform machine learning modeling.

(DOCX)</p

Python script to build networks and extract features.

(DOCX)</p