论文标题

在线社交网络中的自我网络结构及其对信息扩散的影响

Ego Network Structure in Online Social Networks and its Impact on Information Diffusion

论文作者

Arnaboldi, Valerio, Conti, Marco, La Gala, Massimiliano, Passarella, Andrea, Pezzoni, Fabio

论文摘要

在过去的几年中,由于他们在社会中的核心作用,在线社交网络(OSN)吸引了许多研究人员的兴趣。通过对OSN的分析,已经研究了许多社会现象,例如信息之间的信息传播。仍然不清楚的是OSN的微观结构属性(即用户个人网络的属性,也称为自我网络)与这种现象的出现之间的关系。更好地了解这种关系对于为未来的互联网创建服务可能是必不可少的,例如在用户需求和特征上安装了高度个性化的广告。在本文中,我们通过分析大量Facebook和Twitter用户样本的自我网络来弥合这一差距。我们的结果表明,OSN的微观结构属性有趣地与离线形成的社交网络中发现的相似。特别是,在线自我网络显示出与离线相同的结构,其社交联系人以兼容大小和组成的层次排列。从对Twitter自我网络的分析,我们能够找到TIE强度和自我网络圈子对网络中信息传播的直接影响。具体而言,在Twitter中用户和她的朋友之间的直接联系频率(替代领带强度的代理)与用户从朋友产生的推文产生的推文中发出的转发频率之间存在很高的相关性。我们分析了Twitter中标识的每个自我网络层的相关性,发现它们在信息传播中的作用。

In the last few years, Online Social Networks (OSNs) attracted the interest of a large number of researchers, thanks to their central role in the society. Through the analysis of OSNs, many social phenomena have been studied, such as the viral diffusion of information amongst people. What is still unclear is the relation between micro-level structural properties of OSNs (i.e. the properties of the personal networks of the users, also known as ego networks) and the emergence of such phenomena. A better knowledge of this relation could be essential for the creation of services for the Future Internet, such as highly personalised advertisements fitted on users' needs and characteristics. In this paper, we contribute to bridge this gap by analysing the ego networks of a large sample of Facebook and Twitter users. Our results indicate that micro-level structural properties of OSNs are interestingly similar to those found in social networks formed offline. In particular, online ego networks show the same structure found offline, with social contacts arranged in layers with compatible size and composition. From the analysis of Twitter ego networks, we have been able to find a direct impact of tie strength and ego network circles on the diffusion of information in the network. Specifically, there is a high correlation between the frequency of direct contact between users and her friends in Twitter (a proxy for tie strength), and the frequency of retweets made by the users from tweets generated by their friends. We analysed the correlation for each ego network layer identified in Twitter, discovering their role in the diffusion of information.

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