Query hidden attributes in social networks

Azade Nazi, Saravanan Thirumuruganathan, Vagelis Hristidis, Nan Zhang, Khaled Shaban, Gautam Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Micro blogs 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.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
EditorsZhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781479942749
StatePublished - Jan 26 2015
Event14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China
Duration: Dec 14 2014 → …

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259


Conference14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
Period12/14/14 → …

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software


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