Structural link prediction using community information on Twitter
Structural link prediction using community information on Twitter
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DOI：
10.1109/cason.2012.6412391
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发表时间：
2012-11
期刊：
影响因子：
--
通讯作者：
J. Valverde-Rebaza;A. Lopes
中科院分区：
文献类型：
--
作者：
J. Valverde-Rebaza;A. Lopes
目前，社交网络和社交媒体引起了越来越多的研究兴趣。在这种情况下，链接预测是最重要的任务之一，因为它可以预测社交网络中用户成员之间未来关系的存在或缺失。在本文中，我们描述了实验来分析应用集群内和集群间（WIC）度量来预测大规模在线社交网络上未来链接存在的可行性。与无向社交网络相比，有向社交网络受到的关注较少，仍然没有得到很好的理解，这主要是由于不对称链接的出现。WIC方法结合了局部结构相似度信息和社区信息，提高了链路预测精度。我们使用来自Twitter（一个定向和非对称的大型在线社交网络）的真实数据，将WIC测量与基于局部结构相似性的经典测量进行了比较。实验表明，WIC方法可以有效地应用于有向和非对称的大规模网络。此外，它优于所有用于链接预测的比较措施。
Currently, social networks and social media have attracted increasing research interest. In this context, link prediction is one of the most important tasks since it can predict the existence or missing of a future relation between user members in a social network. In this paper, we describe experiments to analyze the viability of applying the within and inter cluster (WIC) measure for predicting the existence of a future link on a large-scale online social network. Compared with undirected social networks, directed social networks have received less attention and still are not well understood, mainly due to the occurrence of asymmetric links. The WIC measure combines the local structural similarity information and community information to improve link prediction accuracy. We compare the WIC measure with classical measures based on local structural similarities, using real data from Twitter, a directed and asymmetric large-scale online social network. Our experiments show that the WIC measure can be used efficiently on directed and asymmetric large-scale networks. Moreover, it outperforms all compared measures employed for link prediction.