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Recommender Systems are designed to analysis the available data in the system to predict user’s desires and provide appropriate personalized suggestions to each user that suits their interests. In this paper, we have developed an explainable medical recommender system that uses graph concepts to provide an interpretable approach to medical data. The presented approach is based on community detection algorithms. It forms a graph between the users based on their similarity scores. Individuals with common interests are then grouped using graph community detection algorithms. Two community detection algorithms have been applied on the graphs of users and physicians in our medical recommender system. The results of applying two community detection algorithms are then used to address the cold start problem. We have identified the most influential users using a graph-based technique that finds the …