| Title | : | Opinion Dynamics in Social Networks: Modeling and Inference |
| Speaker | : | Abir De (Max Planck Institute of Software Systems, Germany) |
| Details | : | Mon, 29 Apr, 2019 11:00 AM @ Turing Hall |
| Abstract: | : | Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where the users often update their opinions about a particular topic by learning from the opinions shared by their friends. Understanding such networked opinion formation process has a large spectrum of applications, for example, poll prediction, brand estimation, etc. Beyond prediction, news and information are often curated by editorial boards, professional journalists who post feeds on the users' walls in order to steer their opinion to a desired state. Therefore, the underlying opinion dynamical process also involves the influence of external sources--- which are difficult to identify. In this talk, I shall present these challenging tasks related to opinion dynamics from all these perspectives--- (a) learning a data-driven model of spontaneous opinion exchange; and (b) demarcating endogenous and exogenous opinion diffusion process in social networks. Finally, I will present some interesting directions for future research.
BIO: Abir De is a postdoctoral researcher in Max Planck Institute for Software Systems at Kaiserslautern, Germany since January 2018. He was hosted by Manuel Gomez Rodriguez. He received his PhD from Department of Computer Science and Engineering, IIT Kharagpur in July 2018. During that time, he was a part of the Complex Network Research Group (CNeRG) at IIT Kharagpur. His PhD work was supported by Google India PhD Fellowship 2013. Prior to that, he did his BTech in Electrical Engineering and MTech in Control Systems Engineering both from IIT Kharagpur. His main research interests broadly lie in modeling, learning and control of networked dynamical processes. Very recently, he started working on deep learning on graphs, for example, deep generative random graph model, deep reinforcement learning of networked processes, etc. His publications can be accessed from here. |
