12/1/22 | 4:15pm | E51-145
Abstract: Individuals interact in real-world to build relationships, share information, and create impacts. These interactions are abstracted by social networks. In this presentation, we cover a series of ongoing studies that integrate social network analysis into operations management. The main challenge lies in the incompatibility between complicated network behaviors and the tractability of downstream optimization problems. Typically, the network effect, which propagates across the network structure and evolves over time, has a complicated form or, even worse, lacks closed-form expression. Therefore, it is difficult to evaluate the influence of social networks on people's behavior, let alone incorporate it into the resulting optimization problems. This challenge is exacerbated by the advent of the digital era, wherein online social networks are typically extensive and expected to expand continuously.
Our work focuses on establishing unifying frameworks to provide simple, efficient, and high-quality solutions to social network-related operational problems. We take advantage of social network data provided by online platforms as well as a wealth of contextual information, and demonstrate how these massive datasets can be linked with analytic models for decision support. More specifically, we provide a complete set of tools, including modeling, estimation, and optimization, to tackle operational problems on social networks. Our work has a variety of applications in practice, such as influence maximization, content promotion, pricing for social network services, etc.
Bio: Max Shen obtained his Ph.D. in Industrial Engineering and Management Sciences from Northwestern University. He joined the department in July 2004. Before that he taught at the Industrial and Systems Engineering Department at the University of Florida. His primary research interests are in the general area of integrated supply chain design and management, and practical mechanism design.