Using Social Network Analysis to Assess Collaborative Networks: A Case Study from the Genebank Platform Evaluation

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What is social network analysis (SNA)?

Social Network Analysis (SNA) is an established method in sociology since the early 20th century that has gained prominence in recent decades due to technological advances. It is versatile and can be applied in a wide range of fields—including economics, biology, medicine, communications, and more—by identifying key actors within a social framework and decoding their interconnections. The approach offers a systematic methodology that employs graph theory to visually represent the structure of connections among entities.

In impact evaluations, SNA can usefully quantify the collaborative efforts of various stakeholders to achieve shared objectives. It can enhance evaluation processes by capturing and visualizing relationship nuances in interventions or programs, revealing insights into collaboration dynamics, identifying strong connections, and pinpointing gaps where interactions can be improved or established—thus boosting a group’s overall effectiveness.

In SNA, networked structures are mapped out, with individual actors—such as people, groups, and organizations—referred to as ‘nodes’, and the relationships or interactions between them considered ‘edges’. These connections can be of any kind, such as family ties, friendships, professional, geographical, institutional, health-related, etc. Several tools can then be employed to study and interpret the mapped structures.

Several CGIAR studies have used SNA as a powerful way to measure the involvement of multiple stakeholders, including an evaluation of the influence and reach of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), and a systematic portfolio review of the International Maize and Wheat Improvement Center (CIMMYT)’s climate change research portfolio.

This blog shares key learnings from the CGIAR’s recent Genebank Platform evaluation. It demonstrates the as-yet-untapped potential of integrating SNA to generate and visualize evidence based on Quality of Science (QoS) evaluation criteria in process and performance evaluations.

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