Query Hidden Attributes in Social Networks
Query Hidden Attributes in Social Networks
复制标题
查询社交网络中的隐藏属性
DOI：
10.1109/icdmw.2014.113
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Gautam Das
中科院分区：
文献类型：
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作者：
Azade Nazi;Saravanan Thirumuruganathan;Vagelis Hristidis;Nan Zhang;K. Shaban;Gautam Das
微博和协作内容网站，如Twitter和亚马逊，在数百万用户中很受欢迎，他们每天都会产生大量的推文、帖子和评论。尽管它们很受欢迎，但这些网站只提供了基本的机制来导航它们的网站，通过编程或通过浏览器，如关键字搜索界面或获取邻居(例如，朋友)界面。许多有趣的问题都不能通过这些界面直接回答，例如，找到洛杉矶的Twitter用户，他们在去年发了一条推文，说“糖尿病”。注意，Twitter编程接口不允许在用户的家庭位置上设置条件。在本文中，我们介绍了一个新的问题，即利用微博和协同内容网站提供的现有搜索机制来查询这些网站的隐藏属性。我们将这些数据源建模为异构图及其两个关键访问接口：本地搜索和内容搜索，分别通过关键字和邻居进行搜索。我们展示了这两种方法中哪一种更适用于哪种类型的隐藏属性搜索。我们在Twitter、Amazon和Rate MD上进行了实验，以评估这些搜索方法的性能。
Microblogs and collaborative content sites such as Twitter and Amazon are popular among millions of users who generate huge numbers of tweets, posts, and reviews every day. Despite their popularity, these sites only provide rudimentary mechanisms to navigate their sites, programmatically or through a browser, like a keyword search interface or a get-neighbors (e.g., Friends) interface. Many interesting queries cannot be directly answered by any of these interfaces, e.g., Find Twitter users in Los Angeles that have tweeted the word "diabetes" in the last year. Note that the Twitter programming interface does not allow conditions on the user's home location. In this paper, we introduce the novel problem of querying hidden attributes in micro blogs and collaborative content sites by leveraging the existing search mechanisms offered by those sites. We model these data sources as heterogeneous graphs and their two key access interfaces, Local Search and Content Search, which search through keywords and neighbors respectively. We show which of these two approaches is better for which types of hidden attribute searches. We conduct experiments on Twitter, Amazon, and Rate MDs to evaluate the performance of the search approaches.