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Discover opinion leader in online social network using firefly algorithm

机译:使用萤火虫算法发现在线社交网络中的意见领袖

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Nowadays, with the widespread access to web 2.0, the social network plays an unbelievable role in knowledge sharing and diffusion of new products. People can share their views and can visit other's opinion about the particular material, news, products, artifacts and, trends, etc. anywhere, anytime, and anywhere. An Opinion leader is a critical person who can change, modify and transform other's view by their knowledge and proficiency. In this article, an innovative approach is proposed to discover the top-N local and global opinion leader within the community and social network respectively. Initially, we identified the community structure within the social network using the modified Louvain method and next identified the opinion leader using a modified firefly algorithm in each community. We also determined the global opinion leader within the same social network using the same firefly algorithm. The proposed approach is exceptionally supportive to expert and intelligent system because it competently discovered the local optimum concurrently in each subgroup of the social network. All the users can update its attractiveness value without any supposition, and as soon as the distance among the user's increases, the other users can automatically create another subgroup in the network and form the local community. In addition, as the population size in the network increases, the entire users measure their prominence simultaneously. Therefore, there is no consequence on computational time and accuracy of the algorithm. Thus, the proposed algorithm Is superlative suitable for discovering the opinion leader in the local community and globally in the social network. For legalized the proposed approach, we implemented our proposed method on synthesized as well as on real dataset. Finally, we concluded that both the recommended procedures are much better concerning the accuracy, precision, recall, and F1-score with the widely used standard Social Network Analysis (SNA) measures. (C) 2018 Elsevier Ltd. All rights reserved.
机译:如今,随着对Web 2.0的广泛访问,社交网络在知识共享和新产品传播中起着不可思议的作用。人们可以在任何地方,任何时间和任何地方共享他们的观点,并可以访问他人对特定材料,新闻,产品,人工制品和趋势等的看法。意见领袖是一个至关重要的人,可以通过他们的知识和熟练程度来改变,修改和转变他人的观点。在本文中,提出了一种创新方法来分别发现社区和社交网络中排名前N位的本地和全球舆论领袖。最初,我们使用改进的Louvain方法在社交网络中确定了社区结构,然后在每个社区中使用了改进的萤火虫算法确定了意见领袖。我们还使用相同的萤火虫算法确定了同一社交网络中的全球舆论领袖。所提出的方法特别支持专家和智能系统,因为它能同时在社交网络的每个子组中发现局部最优。所有用户都可以在没有任何假设的情况下更新其吸引力值,并且只要用户之间的距离增加,其他用户就可以自动在网络中创建另一个子组并形成本地社区。另外,随着网络中人口规模的增加,整个用户会同时衡量其知名度。因此,不影响算法的计算时间和准确性。因此,所提出的算法是最适合在社区和全球范围内发现意见领袖的。为了使提出的方法合法化,我们在合成数据集和真实数据集上实施了提出的方法。最后,我们得出结论,在广泛使用的标准社交网络分析(SNA)措施方面,推荐的两种程序在准确性,准确性,召回率和F1得分方面都更好。 (C)2018 Elsevier Ltd.保留所有权利。

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