Industry–University–Research (IUR) networks are vital for knowledge sharing and collaborative innovation, yet existing network research largely ignores the maintenance cost of inter-organizational ties, which creates persistent theoretical tension between social capital theory (advocating dense connections) and structural hole theory (advocating sparse non-redundant ties). This study constructs a simulation model integrating barter knowledge exchange and multi-dimensional relational maintenance cost loss and systematically simulates the evolution of average knowledge stock (AKS) under regular, small-world and random network structure. The simulation results show that there exists a stable optimal node degree range of 20–40 for IUR actors, which is robust against changes in network scale, initial knowledge endowment and relational cost coefficients. Under moderate technological complexity, small-world networks realize the highest efficiency of knowledge accumulation; when technological complexity rises to a high level, regular networks with local agglomeration advantages become more efficient. This study supplements a cost-based analytical perspective to reconcile the contradiction between two core network theories and provides preliminary simulation evidence for the contingent design of IUR collaborative networks. From a practical perspective, the findings offer reference for adaptive governance of IUR alliances to balance relational costs and knowledge gains and further respond to the United Nations Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Limitations of this simulation-based analysis are clearly acknowledged in the discussion section.