In today’s world scenario, many of the real-life problems and application data can be represented with the help of the graphs. Nowadays technology grows day by day at a very fast rate; applications generate a vast amount of valuable data, due to which the size of their representation graphs is increased. How to get meaningful information from these data become a hot research topic. Methodical algorithms are required to extract useful information from these raw data. These unstructured graphs are not scattered in nature, but these show some relationships between their basic entities. Identifying communities based on these relationships improves the understanding of the applications represented by graphs. Community detection algorithms are one of the solutions which divide the graph into small size clusters where nodes are densely connected within the cluster and sparsely connected across. During the last decade, there are lots of algorithms proposed which can be categorized into mainly two broad categories; non-overlapping and overlapping community detection algorithm. The goal of this paper is to offer a comparative analysis of the various community detection algorithms. We bring together all the state of art community detection algorithms related to these two classes into a single article with their accessible benchmark data sets. Finally, we represent a comparison of these algorithms concerning two parameters: one is time efficiency, and the other is how accurately the communities are detected.

中文翻译：

---

社区检测算法的最新趋势：一项调查

在当今的世界场景中，许多现实生活中的问题和应用数据都可以借助图表来表示。如今，技术日新月异地以非常快的速度发展；应用程序生成大量有价值的数据，因此它们的表示图的大小增加了。如何从这些数据中获取有意义的信息成为研究热点。需要有系统的算法从这些原始数据中提取有用的信息。这些非结构化图在本质上并不分散，但它们显示了它们的基本实体之间的一些关系。基于这些关系识别社区可以提高对图表示的应用程序的理解。社区检测算法是将图形划分为小规模集群的解决方案之一，其中节点在集群内密集连接并且稀疏连接。在过去十年中，提出了许多算法，主要可分为两大类；非重叠和重叠社区检测算法。本文的目的是对各种社区检测算法进行比较分析。我们将与这两个类别相关的所有最先进的社区检测算法与可访问的基准数据集整合到一篇文章中。最后，我们对这些算法的两个参数进行比较：一个是时间效率，另一个是社区检测的准确度。在过去十年中，提出了许多算法，主要可分为两大类；非重叠和重叠社区检测算法。本文的目的是对各种社区检测算法进行比较分析。我们将与这两个类别相关的所有最先进的社区检测算法与可访问的基准数据集整合到一篇文章中。最后，我们对这些算法的两个参数进行比较：一个是时间效率，另一个是社区检测的准确度。在过去十年中，提出了许多算法，主要可分为两大类；非重叠和重叠社区检测算法。本文的目的是对各种社区检测算法进行比较分析。我们将与这两个类别相关的所有最先进的社区检测算法与可访问的基准数据集整合到一篇文章中。最后，我们对这些算法的两个参数进行比较：一个是时间效率，另一个是社区检测的准确度。本文的目的是对各种社区检测算法进行比较分析。我们将与这两个类别相关的所有最先进的社区检测算法与可访问的基准数据集整合到一篇文章中。最后，我们对这些算法的两个参数进行比较：一个是时间效率，另一个是社区检测的准确度。本文的目的是对各种社区检测算法进行比较分析。我们将与这两个类别相关的所有最先进的社区检测算法与可访问的基准数据集整合到一篇文章中。最后，我们对这些算法的两个参数进行比较：一个是时间效率，另一个是社区检测的准确度。