Powered by big data infrastructures, social network platforms are gathering data on many aspects of our daily lives. The online social world is reflecting our physical world in an increasingly detailed way by collecting people's individual biographies and their various of relationships with other people. Although massive amount of social data has been gathered, an urgent challenge remain unsolved, which is to discover meaningful knowledge that can empower the social platforms to really understand their users from different perspectives.
Motivated by this trend, my research addresses the reasoning and mathematical modeling behind interesting phenomena on social networks. Proposing graph based data mining framework regarding to heterogeneous data sources is the major goal of my research. The algorithms, by design, utilize graph structure with heterogeneous link and node features to creatively represent social networks' basic structures and phenomena on top of them.
The graph based heterogeneous mining methodology is proved to be effective on a series of knowledge discovery topics, including network structure and macro social pattern mining such as magnet community detection (87), social influence propagation and social similarity mining (85), and spam detection (86). The future work is to consider dynamic relation on social data mining and how graph based approaches adapt from the new situations.
|Advisor:||Yu, Philip S.|
|Commitee:||Chen, Chen, Liu, Bing, Tunkelang, Daniel, Yu, Philip S., Ziebart, Brian|
|School:||University of Illinois at Chicago|
|School Location:||United States -- Illinois|
|Source:||DAI-B 76/05(E), Dissertation Abstracts International|
|Keywords:||Graph algorithms, Influence maximization, Magnet community detection, Orgchart discovery, Review spam detection, Social network mining|
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