•Dynamic network analysis and statistical prediction:Study-ing the dynamics is of utmost important. Dynamic network analysis is an emerging field that brings together social net-work analysis (SNA), and link analysis in network science. There are different streams of dynamic network analysis, one of them is a statistical one[9]. Dynamic network analysis takes into account the temporal and dynamic analysis of a network. Among various types of models proposed for statistical model-ing and prediction of network data, two stand out to be most promising. The first type consists of exponential random graph models[32], in which ties among nodes are assumed to be random variables and dependencies among these random vari-ables are further imposed. The second type consists of latent social space models[19], which postulate the existence of a latent space and further assume that ties as random variables are determined by the positions of actors on the latent social space. Both models can be used to statistically infer fundamen-tal rules and patterns of a social network based from observed data. An important research direction is to use these method-ologies in the context of OC and to further advance them for the purpose of developing proper prediction models dynamic multi-modal networks. As these types of roles have not been investigated in large sociotechnical systems where collabora-tion is not tied to social ties but to practice and activity ties, further advances to such prediction models are needed. In ad-dition, it is crucial to identify new statistical models to identify emerging ‘enacted’ roles that are less structurally oriented but result from interactions between users, or user-goods contri-butions.