Unveiling Actors in Collaboration Networks
by Nicolas Sacchetti
At the 4POINT0 Conference on Policies, Processes, and Practices for the Performance of Innovation Ecosystem (P4IE 2022), Mahsa Noori Najafi, a Ph.D. student at Concordia University supervised by Drs. Schiffauerova and Ebadi, presented her research titled Using Simulation to Investigate the Role Of Critical Actors In a Collaborative Ecosystem. The research focuses on individual-level dynamics within collaboration networks.
Moving away from traditional firm-level analysis, Najafi’s work explores the influence of individual actors within these networks, examining their impact on network productivity and knowledge creation.
Central to Najafi’s research is the use of an Agent-Based Model, a computer simulation framework for autonomous entities with diverse characteristics and behaviors. This model plays a crucial role in simulating and analyzing the behaviors of individuals within the network.
In her research, Najafi characterizes individual researchers into distinct groups based on their research performance and positions within the network, namely gatekeepers, popular, loyal, and embedded scientists. This categorization facilitates a deeper understanding of collaboration dynamics. The study’s significant contribution lies in developing the agent-based model to define these critical actors and simulate their collaborative behaviors based on various strategies in each iteration. Additionally, a modular Python design enhances the model’s flexibility, allowing adaptation to various scenarios.
Looking ahead, the research focuses on recalculating analyses across the entire dataset and extending the use of machine learning algorithms. This includes optimizing collaboration numbers, assessing probabilities of repeated collaborations, understanding network age, and activating nodes in each iteration. The team plans to explore algorithms beyond logistic regressions and consider factors like the average lifetime career of network nodes.
Mahsa Noori Najafi’s research offers insightful perspectives into the functioning of collaboration networks at the individual level. Her work not only contributes to the academic understanding of these networks but also has potential applications in optimizing real-world collaborative ecosystems.
Ce contenu a été mis à jour le 2023-11-10 à 21 h 15 min.