Survey on Social Community Detection <s> Evaluation methods for community detection <s> We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of community detection to problems in statistical physics and computational optimization. <s> BIB001 </s> Survey on Social Community Detection <s> Evaluation methods for community detection <s> The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i.e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e.g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks. <s> BIB002
Validation is a key issue: How is it possible to verify that the communities identified are actually the appropriate ones? How is it possible to compare results between two distinct algorithms and declare one better than the other? Several methods may be proposed. One simple sentence from a survey BIB001 reminds us of the great difficulty involved in evaluating community detection methods: "Now that we have all these ways of detecting communities, what do we do with them?" No evaluation methods are actually given. In his survey, Fortunato BIB002 provides an effective analysis of evaluation methods in proposing three steps: benchmarks, evaluation measures, and comparative evaluation results. Papadopoulos' survey  merges evaluation with various community definitions; like other surveys available, it does not pay great attention to evaluation, except for presenting applications on well-known cases and extensive practical social networks. Most validation methods have been designed for the methods in cell A1 of Tables 1 and 2 . As a matter of fact, most detection methods may be compared to "clustering" algorithms as regards evaluation. It is well known that clustering yields results that are difficult to evaluate. This same situation is encountered in the area of community detection. Some standard evaluation methods do however emerge. This section will start by presenting several measures of potential use in evaluating the results of community detection methods. Afterwards, evaluation benchmarks will be introduced before reviewing a number of evaluation methods.