Social networks influence health outcomes, yet declining health can also reshape social ties. While prior research has focused on constrained settings, the impact of health on social networks in fully voluntary contexts remains underexplored. This study examines the reciprocal relationship between health and social networks in voluntary settings, assessing whether previously observed patterns persist. We analyzed three-wave longitudinal whole network data from two voluntary clubs (N = 102, mean age = 54 years) in North-Rhine Westphalia, Germany, using Stochastic Actor-Oriented Models to distinguish between selection and influence effects across self-rated, mental, and physical health measures. Our analyses suggest diverging patterns observed in more constrained settings. We found no evidence of peer influence on health across any measures. While self-rated health showed some evidence of selection effects, social avoidance was limited to individuals with poor physical health. Notably, we found no evidence of withdrawal; instead, individuals with poorer health were more likely to nominate others in the network, suggesting they actively sought social connections as a compensatory strategy. These findings challenge existing assumptions about health-based network dynamics, emphasizing the need to reconsider how social networks function in voluntary contexts. Future research should explore how the degree of setting constraints shape health-related network dynamics.

We review the empirical comparison of Stochastic Actor-oriented Models (SAOMs) and Temporal Exponential Random Graph Models (TERGMs) by Leifeld & Cranmer in this journal [Network Science 7(1):20–51, 2019]. When specifying their TERGM, they use exogenous nodal attributes calculated from the outcome networks’ observed degrees instead of endogenous ERGM equivalents of structural effects as used in the SAOM. This turns the modeled endogeneity into circularity and obtained results are tautological. In consequence, their out-of-sample predictions using TERGMs are based on out-of-sample information and thereby predict the future using observations from the future. Thus, their analysis rests on erroneous model specifications that invalidate the article’s conclusions. Finally, beyond these specific points, we argue that their evaluation metric—tie-level predictive accuracy—is unsuited for the task of comparing model performance.

The temporal exponential random graph model (TERGM) and the stochastic actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these models theoretically, via simulation, and through a real-data example in order to assess their relative strengths and weaknesses. Though we do not aim to make a general claim about either being superior to the other across all specifications, we highlight several theoretical differences the analyst might consider and find that with some specifications, the two models behave very similarly, while each model out-predicts the other one the more the specific assumptions of the respective model are met.