The Role Of Network Embeddedness On The Selection Of Collaboration Partners: An Agent-Based Model With Empirical Validation
Frank Schweitzer, Antonios Garas, Mario V. Tomasello, Giacomo Vaccario and Luca Verginer
Advances in Complex Systems (2022)
Abstract
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.
