Acknowledgment: This material is based upon work supported by the National Science Foundation under Grant No. 1619458
Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Award Number: 1619458
Expected Duration: 09/2016-08/2020
Award Title: III: Small: Robustness in Social Network Analysis: Models, Inference, and Algorithms
Social networks - online and offline - play a large role in people's daily lives. In return, they implicitly contain a large amount of information about individuals and their traits and preferences. The burgeoning field of "Social Network Analysis" focuses on extracting useful insights from such social network data. Implemented or envisioned applications range from learning about the nature and driving forces behind human interactions, to targeted product or activity recommendations and even homeland security. Contrary to other networks, such as transportation or computer networks, massive uncertainty and noise are practically always associated with social network data: data pertaining to individuals are often not observable, or are observed incorrectly. The primary goal of this project is to understand the risks and implications of such noisy data, and to design network analysis algorithms that are significantly more robust to noise and missing data. Given the importance that mathematical models play in social networks analysis, a closely related thread of the project is to analyze the fit between typical social network models and real-world data, in particular regarding high-level connectivity properties. Specifically, three connected research thrusts are being explored:
The proposed research has the potential to impact the way in which social network inference and optimization are addressed. The PIs are committed to a suite of activities, among them inclusion of undergraduate students in the proposed research and outreach to local high school students, for broader impacts.
Karishma Sharma, Feng Qian, He Jiang, Natali Ruchansky, Ming Zhang, Yan Liu.
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ACM Transactions on Intelligent Systems and Technology (TIST)
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