Sensible Graph Analytics for Massive Interlinked Data and Social Networks

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Data is generated at an exponential rate from millions of people around the world, large-scale open-data movements, scientific experiments, business information systems, social media, and so on. This data is often represented as a web of facts that link entities (people, organizations, and objects) to each other. Also, entities in different data-sources are interlinked together due to ever-increasing data integration initiatives. The result is an enormous amount of data which is represented as graphs (networks), where the nodes are entities and the edges are the links between them. Graphs are also the backbone of modeling online social networks and diffusion of information. Social media and networks can reach out to billions of users worldwide and spread out vital information in a very short amount of time. Most businesses and other organizations have understood the tremendous power of social media and networks; however, finding the most effective ways to tap into this potential is still an elusive goal. The unifying theme and novelty of our research is in addressing simultaneously the real life challenges of volume, uncertainty, and impreciseness of data links and social connections. Efficiently computing reliable graph-analytics under these conditions becomes much harder. Some of the questions we strive to answer are: How to extract insightful, high-confidence patterns from large graph databases under uncertainty and impreciseness? How to find influential individuals and communities in large social networks under uncertain influence patterns and competitive information providers? How to limit the spread of "bad" information and accelerate the spread of "good" information? How to make use of modern systems for massive computing to solve computational challenges in analyzing very large graphs of interlinked data and social networks? Our research has the potential for great impact in a multitude of data analysis activities in many diverse areas of science, engineering, and business.