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A Comparison of Overlapping Community Detection in Large Complex Network

机译：大型复杂网络中重叠社区检测的比较

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摘要

Many large scale network contains community structure, that nodes are densely connected with own group and less connected to other groups. Community contains users those having similar characteristics from other groups or community. Now days, more number of people are paying attention on social network for information, news, comments, likes etc. Due to this social network sites generates large number of data. These issues often make social network data very complex to analyze manually. In network, there may be possibilities that one node may belongs to one or more than one groups that is called overlapping of nodes. Possibility of overlapping community is high in real world network. There are many fields in which community detection is necessary for example in politics, business, news, and social network like Facebook, Twitter, and LinkedIn etc. In social network large number of overlapping community is available, for analysis of this type of communities or groups in network is tedious task. Therefore research work is based on a heuristic approach to discover overlapped community in large complex network.
机译：许多大型网络包含社区结构，即节点与自己的组密集连接，而与其他组的连接较少。社区包含与其他组或社区具有相似特征的用户。如今，越来越多的人开始关注社交网络上的信息，新闻，评论，喜欢等内容。由于这种社交网络，站点会生成大量数据。这些问题通常使社交网络数据非常难以手动分析。在网络中，一个节点可能属于一个或多个组，这被称为节点重叠。在现实世界中，社区重叠的可能性很高。在许多领域中，例如在政治，商业，新闻和社交网络（例如Facebook，Twitter和LinkedIn）等社区检测都是必需的。在社交网络中，可以使用大量重叠的社区来分析此类社区或社区。网络中的群体是繁琐的任务。因此，研究工作基于启发式方法，以发现大型复杂网络中的重叠社区。

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