The aim of graph clustering is to define compact and well-separated clusters from a given graph. Cluster's compactness depends on datasets and clustering methods.In order to provide evaluation of graph clustering quality, many different indices have been proposed in previous work. Indices are used to compare different graph partitions but also different clustering techniques. Moreover, some clustering techniques are based on index optimization. Indices can also be added as visual tips in graph layouts. Despite the importance of the subject, little indices can not be easily compared or interpreted.In this paper, we provide a unified and synthetic view of indices used in graph clustering area and discuss them. We also propose several enhanced measures.