A Comparison of Overlapping Community Detection in Large Complex Network

2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC) Pub Date : 2019-03-01 DOI:10.1109/ICRAECC43874.2019.8994969

Khyati Fatania, D. Joshi, T. Patalia, Yasmin Jejani

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引用次数: 0

Abstract

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.

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大型复杂网络中重叠社区检测方法的比较

许多大型网络都包含社区结构，即节点与本组的连接密集，与其他组的连接较少。社区包含来自其他组或社区的具有相似特征的用户。现在，越来越多的人关注社交网络的信息，新闻，评论，喜欢等。因此，社交网站产生了大量的数据。这些问题通常会使社交网络数据变得非常复杂，难以手工分析。在网络中，一个节点可能属于一个或多个组，这种情况称为节点重叠。在现实网络中，社区重叠的可能性很高。在许多领域，社区检测是必要的，例如在政治、商业、新闻和社交网络，如Facebook、Twitter和LinkedIn等。在社会网络中存在着大量的重叠社区，对网络中这种类型的社区或群体进行分析是一项繁琐的任务。因此，研究工作是基于启发式方法来发现大型复杂网络中的重叠社区。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)

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