Mining diversity on social media networks

Mining diversity on social media networks
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挖掘社交媒体网络上的多样性

DOI:
10.1007/s11042-010-0568-1
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发表时间:
2010
影响因子:
3.6
通讯作者:
Yang, Shiqiang
Yang, Shiqiang
中科院分区:
计算机科学4区
文献类型:
--
作者:
Liu, Lu;Zhu, Feida;Jiang, Meng;Han, Jiawei;Sun, Lifeng;Yang, Shiqiang

文献摘要

参考文献

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多媒体技术的快速发展和网络带宽的增加的可用性已经由于近年来所有不断蓬勃发展的社交媒体和社交网站(例如,Flickr、Youtube、MySpace、Facebook等社交网络分析因此成为吸引学术界和工业界热情的关键问题。然而,一个重要的措施,捕捉参与者的多样性在网络中已在很大程度上被忽视在以前的研究。也就是说,多样性表征了给定节点与其对等节点连接的多样性。在本文中,我们对这一概念进行了全面研究。我们首先列出了两个标准,捕捉多样性的语义含义,然后提出了一个兼容的定义,这是简单到足以嵌入的想法。基于该方法,我们不仅可以衡量用户的社交性和兴趣多样性,而且还可以衡量社交媒体的用户多样性。提出了一种适用于动态网络的top-k多样性排序算法。在合成和真实的社交媒体数据集上的实验给出了有趣的结果,其中具有高扩散性的单个节点是直观的。
The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube, MySpace, Facebook, etc. Social network analysis has therefore become a critical problem attracting enthusiasm from both academia and industry. However, an important measure that captures a participant’sdiversityin the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. We first lay out two criteria that capture the semantic meaning of diversity, and then propose a compliant definition which is simple enough to embed the idea. Based on the approach, we can measure not only a user’s sociality and interest diversity but also a social media’s user diversity. An efficient top-k diversity ranking algorithm is developed for computation on dynamic networks. Experiments on both synthetic and real social media datasets give interesting results, where individual nodes identified with high diversities are intuitive.
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