CRII: III: Learning networks from strategic decisions: enabling network intervention and revealing social privacy risks without structural information

University Of Texas At Austin, Austin TX

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The rise of digital platforms and cloud-based products changed business operations and consumer decision-making. The bolstered connections improve the effectiveness of dynamic pricing, marketing campaigns, and large-scale behavioral change on digital platforms. On crowdsourced review platforms such as Yelp and Google Review, publicly ratings and reviews reduce consumer information search friction; on E-commerce retailers such as Amazon and Alibaba, information about products strengthens consumers' trust in the products. Integrating machine learning methods and rich behavioral data improve consumers' experience, accrues revenues, and guides manufacturers' and retailers' pricing strategies. Even though connections among individuals are powerful in a wide range of applications: it is not always available for various reasons: (1) network data is costly to collect; (2) network data may be too sensitive and confidential to share; (3) network data is dynamic and static data's accuracy may decay over time. In the meantime, strengthened social interaction and data integration pose privacy risks, leading to data security issues at a systematic level. This project lays the groundwork for learning social network structures based on large-scale behavioral data. Two inversely-related issues motivate this research: 1) How to design network interventions to leverage social externality when structural data is unavailable? 2) Does publicly available behavioral data pose social privacy risk in leaking social network information? This project studies the general and fundamental problem of learning the network structures and approximating utility functions based on observed decisions. This project first analyzes this network learning problem's fundamental and conditional identifiability by imposing a linear-quadratic network game structure in a network of strategic decision-makers. It will extend this linear-quadratic game to a broader class of network games. This project further develops a generative deep learning framework to approximate human decision-making processes on social networks. Finally, the researchers will demonstrate the practical value of this research by applying the theory and approach to network intervention (e.g., information diffusion and influence maximization) and privacy risk evaluations (e.g., shadow profiling and social attack) using three large-scale digital behavioral data and two small-scale experimental data. This interdisciplinary project builds upon and contributes to game theory, machine learning, network science, and management science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.