The role of network embeddedness on the selection of collaboration   partners: An agent-based model with empirical validation
  We use a data-driven agent-based model to study the core-periphery structure of two collaboration networks, R&D alliances between firms and co-authorship relations between scientists. To characterize the network embeddedness of agents, we introduce a coreness value, obtained from a weighted $k$-core decomposition. We study the change of these coreness values when collaborations with newcomers or established agents are formed. Our agent-based model is able to reproduce the empirical coreness differences of collaboration partners and to explain why we observe a change in partner selection for agents with high network embeddedness. 
Frank Schweitzer, Antonios Garas, Mario V. Tomasello, Giacomo   Vaccario, Luca Verginer
physics.soc-ph cs.SI