Degree and centrality-based approaches in network-based variable selection: Insights from the Singapore Longitudinal Aging Study.

We describe a network-based method to obtain a subset of representative variables from clinical data of subjects of the second Singapore Longitudinal Aging Study (SLAS-2), while preserving to a good extent the predictive performance of the full set with regards to a multi-faceted index of successful aging, SAGE. To examine differences in predictive performance of high-degree nodes ("hubs") and high-centrality ones ("cores"), we implement four subsetting strategies (two degree-based, two centrality-based) and obtain four surrogate sets of variables, which we use as input features for machine learning models to predict the SAGE index of subjects. All four models have variables belonging to the physical, cardiovascular, cognitive and immunological domains among their fifteen most important predictors.

Effects of Cultural Tightness-Looseness and Social Network Density on Expression of Positive and Negative Emotions: A Large-Scale Study of Impression Management by Facebook Users.

Using data from 13,789 Facebook users across U.S. states, this study examined the main effects of societal-level cultural tightness-looseness and its interaction effects with individuals' social network density on impression management (IM) in terms of online emotional expression.

Health and disease phenotyping in old age using a cluster network analysis.

Human ageing is a complex trait that involves the synergistic action of numerous biological processes that interact to form a complex network. Here we performed a network analysis to examine the interrelationships between physiological and psychological functions, disease, disability, quality of life, lifestyle and behavioural risk factors for ageing in a cohort of 3,270 subjects aged ≥55 years. We considered associations between numerical and categorical descriptors using effect-size measures for each variable pair and identified clusters of variables from the resulting pairwise effect-size network and minimum spanning tree.