Context: The world is experiencing a boom of data throughout several fields such as finance, engineering, medicine, crime and security etc. More and more problems associated with these fields are starting to be modeled using networks and dynamic and/or static graphs. This has led to further exploration of these graphs, their community structures and the associated community detection methods. Objective: This paper aims at studying a set of community detection approaches and analyzing their performance in terms of both external and internal criteria, including rand index, adjusted rand index, variation of information and normalized mutual information. Method: The chosen set of algorithms is thoroughly studied. Amongst this set, each algorithm is compared with the other in the set and the result is recorded numerically. These numerical values are recorded in a tabular format for better understanding of the reader. Each table is made specific to the criterion used to compare the algorithms. Altogether, a total of 4 tables are obtained for each of the metric used for the comparative analysis. Results: On comparing the algorithms with each other, the results are tabulated in a symmetric matrix whose values along the diagonal have an associated significance, which is described in the following sections. Conclusion: This paper presents a comparative analysis of the community detection algorithms against a set of external and internal criteria. Further, it lists some challenges faced by the research community in the study of these algorithms. The last section gives a summary of the common applications of community structure and detection methods.

背景：世界正在金融，工程，医学，犯罪和安全等多个领域中经历着大量的数据。越来越多的与这些领域相关的问题开始使用网络以及动态和/或静态图来建模。这导致对这些图形，它们的社区结构以及相关的社区检测方法的进一步探索。目的：本文旨在研究一套社区检测方法，并从外部和内部标准（包括兰德指数，调整后的兰德指数，信息变化和标准化的互信息）分析其性能。方法：对所选算法集进行了深入研究。在该集合中，将每个算法与集合中的其他算法进行比较，并以数字形式记录结果。这些数值以表格格式记录，以更好地理解读者。每个表都特定于用于比较算法的标准。对于用于比较分析的每个指标，总共可获得4个表。结果：在将算法相互比较时，将结果列表在一个对称矩阵中，该矩阵沿对角线的值具有关联的重要性，以下各节对此进行了描述。结论：本文针对一组内部和外部标准对社区检测算法进行了比较分析。此外，它列出了研究人员在研究这些算法时面临的一些挑战。最后一部分总结了社区结构和检测方法的常见应用。