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Degree centrality, eigenvector centrality and the relation between them in Twitter

机译：Twitter中的度中心，特征向量中心及其之间的关系

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摘要

In Social Media the directed links formed between the users, are used for the transfer of information. Based on previous research, the rate of information transfer in a social network depends on the strength of connections of the user in the network, which is measured by the centrality value. In this paper, based on data collected from Twitter, we perform an analysis of eigenvector centrality approach of finding the influential users. We investigate the variation in indegree and eigenvector centrality of users participating in a hashtag in Twitter, with respect to change in the amount of interactions. Here interactions are: tweets, mentions and replies. We also investigate the relationship between indegree and eigenvector centrality in a given hashtag. We make the following interesting observations. First, in Twitter, users with high eigenvector centrality need not be influential users. Second, in a given hashtag, there is an increase in users with both high indegree and eigenvector centrality when there are more user interactions. Here interactions are: tweets, mentions and replies, indicating both indegree and eigenvector centrality should be considered when finding influential users. Third, there is a positive correlation between indegree and eigenvector centrality.
机译：在社交媒体中，用户之间形成的定向链接用于信息传递。根据先前的研究，社交网络中信息的传输速率取决于网络中用户的连接强度，该强度由中心值来衡量。在本文中，基于从Twitter收集的数据，我们对特征向量中心性方法进行了分析，以找到有影响力的用户。我们调查了参与Twitter中的主题标签的用户的度数和本征向量中心性的变化，以反映交互量的变化。这里的交互是：推文，提及和回复。我们还研究了给定主题标签中度数和特征向量中心性之间的关系。我们进行以下有趣的观察。首先，在Twitter中，特征向量中心度高的用户不必是有影响力的用户。其次，在给定的主题标签中，当用户互动更多时，具有较高度数和特征向量中心度的用户就会增加。这里的交互是：推文，提及和回复，表明在寻找有影响力的用户时应同时考虑度数和特征向量的中心性。第三，度数和特征向量中心性之间存在正相关。

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