Social Network Measures for Nosduocentered Networks, their Predictive Power on Performance

Our purpose in this article is to define a network structure which is based on two egos instead of the egocentered (one ego) or the complete network (n egos). We describe the characteristics and properties for this kind of network which we call “nosduocentered network”, comparing it with complete and egocentered networks. The key point for this kind of network is that relations exist between the two main egos and all alters, but relations among others are not observed. After that, we use new social network measures adapted to the nosduocentered network, some of which are based on measures for complete networks such as degree, betweenness, closeness centrality or density, while some others are tailor-made for nosduocentered networks. We specify three regression models to predict research performance of PhD students based on these social network measures for different networks such as advice, collaboration, emotional support and trust. Data used are from Slovenian PhD students and their supervisors. The results show that performance for PhD students depends mostly of the emotional network, because it is significant for all three models. Trust and collaboration networks are significant for two models and advice is not significant for any model. As regards network measures, classic and tailor-made measures are about equally good. Measures related to the total intensity of contacts (e.g., density, degree centralization and size) seem to work best to predict performance.