ArticlePloS one2022
Impact of network density on the efficiency of innovation networks: An agent-based simulation study.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Data-driven network diagnostics for optimizing POCUS management: an actionable hospital analytical approach.BMC health services research · 2026Article
- Building bridges, breaking silos: a social network analysis of collaboration in an implementation science center for addiction health services.Translational behavioral medicine · 2026Article
- Cross-modal BERT model for enhanced multimodal sentiment analysis in psychological social networks.BMC psychology · 2025Article
- The impact of pilot policy for innovative industrial clusters on green innovation efficiency.Scientific reports · 2025Article
- Exploring the role of information networks in promoting sustainable rural production in Iran.Scientific reports · 2025Article
- Identifying intervention points to increase the diversity of a research partnership for cancer equity: a descriptive social network analysis.Translational behavioral medicine · 2025Article
- Article
- Executive function and underlying brain network distinctions for callous-unemotional traits and conduct problems in adolescents.bioRxiv : the preprint server for biology · 2023Article
- Social Network Analysis of the Schistosomiasis control program in two local government areas in Oyo state, Nigeria: Insights for NTD elimination plans.PLoS neglected tropical diseases · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
Network density is an important attribute that affects the efficiency of innovation networks. However, the understanding of how network density affects the innovation efficiency of innovation networks is still unclear and even controversial. This paper uses a multiagent simulation method to study this problem. First, an innovation simulation model is established to describe the generation process of innovations in the context of network innovation, and a classical random network model is used to generate a test set of structures with different network densities. Then, the innovation model is run on the test set of networks to obtain the innovation efficiency of the structures with different network densities. The result shows that for explorative innovation, high network density is more conducive to improving innovation efficiency, and for exploitative innovation, low network density is more conducive to improving innovation efficiency. However, when network density is small enough to destroy network connectivity, it will lead to a large risk of innovation failure. Finally, the reasons for the results are further analyzed, and the theoretical and practical significance of the conclusions are discussed.
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