The CLARANS algorithm is a variation of k-medoids that can be used for clustering large graph data. It offers several advantages, including the ability to work with any distance metric and the fact that medoids returned by the algorithm can serve as typical examples for the clusters they define. However, it also has some drawbacks, such as requiring the calculation of distances between all pairs of nodes in the graph, which can be slow for large graphs. The CLARANS algorithm was developed to extend k-medoids to larger datasets than were practical with earlier algorithms, and it uses a randomized search approach to find good approximations of the optimal solution. The algorithm has been implemented in Python and Neo4j Graph Data Science, and it has been tested on two graph datasets, one small and one large, showing promising results compared to other k-medoid algorithms like FasterPAM. CLARANS is a good choice for medium and large graphs where pre-computation of distances between all pairs of nodes is not feasible.