EAGER: Harnessing the Power of Graph Data Analytics

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The rapid growth of user generated content in online social networks forms an abundant source of big graph data. This project will develop efficient techniques for extracting information from such graph data. Specifically, this EAGER project will develop efficient algorithms for the following tasks: (1) machine learning and sentiment analysis, such as large vocabulary conversational speech recognition, multi-label learning, and multi-polarity sentiment analysis, and (2) effector detection and related issues, such as business expansion and rumor blocking. The advanced optimization techniques developed in this project will significantly improve the performance of the existing data mining algorithms and the theoretical study of efficient approximation techniques. In addition, this project will provide an excellent platform for educating students with hands-on experience. To develop the efficient algorithms described, the project will take advantage of recent development in nonlinear combinatorial optimization, and will formulate new methodologies using discrete convex analysis for submodular functions and duality theory for the discrete DC (different of convex) programming, as well as reverse sampling techniques for probabilistic objective functions. These new methodologies will enrich the throughput of machine learning and sentiment analysis. Since nonlinear combinatorial optimization is still in its early stages, exploratory and innovative research on efficient approximation algorithm will be performed based on fresh fundamental theoretical developments. A detailed evaluation will be conducted based on several real world data sets.