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你是谁你知道:推断用户配置文件中的在线社交网络

You Are Who You Know: Inferring User Profiles in Online Social Networks
课程网址: http://videolectures.net/wsdm2010_mislove_yawyk/  
主讲教师: Alan Mislove
开课单位: 西北大学
开课时间: 2010-04-12
课程语种: 英语
中文简介:
在线社交网络现在是用户连接,表达自己和共享内容的流行方式。今天的在线社交网络中的用户经常发布个人资料,其中包括地理位置,兴趣和参加的学校等属性。这些简档信息在站点上用作分组用户,共享内容以及建议可能受益于交互的用户的基础。但是,实际上并非所有用户都提供这些属性。在本文中,我们提出一个问题:给定在线社交网络中某些用户的属性,我们可以推断剩余用户的属性吗?换句话说,用户的属性是否可以与社交网络图结合用于预测网络中另一个用户的属性?为了回答这个问题,我们从两个社交网络收集细粒度数据并尝试推断用户配置文件属性。我们发现具有共同属性的用户更有可能成为朋友并且通常形成密集社区,我们提出了一种推断用户属性的方法,该方法受到以前检测社交网络中社区的方法的启发。我们的结果表明,当在少至20%的用户上提供信息时,可以高精度地推断某些用户属性。
课程简介: Online social networks are now a popular way for users to connect, express themselves, and share content. Users in today’s online social networks often post a profile, consisting of attributes like geographic location, interests, and schools attended. Such profile information is used on the sites as a basis for grouping users, for sharing content, and for suggesting users who may benefit from interaction. However, in practice, not all users provide these attributes. In this paper, we ask the question: given attributes for some fraction of the users in an online social network, can we infer the attributes of the remaining users? In other words, can the attributes of users, in combination with the social network graph, be used to predict the attributes of another user in the network? To answer this question, we gather fine-grained data from two social networks and try to infer user profile attributes. We find that users with common attributes are more likely to be friends and often form dense communities, and we propose a method of inferring user attributes that is inspired by previous approaches to detecting communities in social networks. Our results show that certain user attributes can be inferred with high accuracy when given information on as little as 20% of the users.
关 键 词: 社会化网络; 利益; 共享内容
课程来源: 视频讲座网
最后编审: 2020-05-31:吴雨秋(课程编辑志愿者)
阅读次数: 62