Hybrid machine learning approach for community and overlapping community detection in social network,Transactions on Emerging Telecommunications Technologies

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Hybrid machine learning approach for community and overlapping community detection in social network
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-11-06 , DOI: 10.1002/ett.4161
Divyapushpalakshmi M ₁ , Ramalakshmi R ₁

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Social Network Analysis (SNA) is one of the significant fields of sociology, which probes many researchers toward it. In SNA, overall social network (such as Facebook, Twitter, and so on) structure is learned by community detection process. However, dynamic nature and involvement of numerous users in social network limits the accuracy of community detection process. In this research work, we propose a novel dual examining and hybrid machine learning approaches for community and overlapping community detection. We collect recent tweets from twitter social network in real time as training dataset. An undirected graph is constructed over collected twitter dataset to enhance the community detection process. Upon constructed graph, informative examining and structural examining processes are applied. Informative examining results with informative factor whereas structural examining identifies influencing node. Structural examining is performed using Particle Swarm Optimization algorithm based on centrality factor and node strength factor. By utilizing results from dual examining processes, community detection is performed by a hybrid machine learning approach Naïve Bayes with Firefly Algorithm. Overlapping community detection is followed up to the community detection using Fuzzy with neural network classifier. Here, Jaccard Similarity factor, overlapping coefficient, and modularity are considered in fuzzy with neural network. Extensive simulation results show better performance than single examining methods in terms of accuracy of mutual information, ratio of community, run time, and average degree in proposed hybrid machine learning approach-based community detection.

中文翻译：

社交网络中社区和重叠社区检测的混合机器学习方法

社会网络分析（SNA）是社会学的重要领域之一，许多研究人员对此进行了探索。在 SNA 中，整个社交网络（如 Facebook、Twitter 等）结构是通过社区检测过程来学习的。然而，社交网络中众多用户的动态性质和参与限制了社区检测过程的准确性。在这项研究工作中，我们提出了一种新颖的双重检查和混合机器学习方法，用于社区和重叠社区检测。我们从 twitter 社交网络实时收集最近的推文作为训练数据集。在收集的 twitter 数据集上构建无向图，以增强社区检测过程。在构造图后，应用信息检查和结构检查过程。信息性检查结果具有信息性因素，而结构性检查识别影响节点。使用基于中心性因子和节点强度因子的粒子群优化算法进行结构检查。通过利用双重检查过程的结果，社区检测由混合机器学习方法 Naïve Bayes 和 Firefly 算法执行。重叠社区检测之后是使用带有神经网络分类器的模糊的社区检测。在这里，Jaccard 相似因子、重叠系数和模块化在神经网络中被考虑模糊。广泛的模拟结果表明，在互信息的准确性、社区比率、运行时间、

更新日期：2020-11-06

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