Strategic and Computational Aspects of Social Network Science

Speaker:  Ramasuri Narayanam – Bangalore, India
Topic(s):  Computational Theory, Algorithms and Mathematics

Abstract

This lecture provides the conceptual underpinnings of the use of game theoretic models as well as online multi-agent learning models in social network analysis and brings out how these models supplement and complement existing approaches for social network analysis. The first part of the lecture provides rigorous foundations of relevant concepts in game theory, mechanism design, network science, and online learning in multi-agent network systems. The second part of the lecture brings out how game theoretic approach and online multi-agent learning approach help analyze key problems in network science better and also how to apply these technical concepts to problem solving in a rigorous way. In particular, it presents a comprehensive study of a few contemporary and pertinent problems in social networks such as social network formation games, social network monetization, design of incentive mechanisms, and economics of networks.

This lecture is targeted at several categories of audience. It targets researchers, graduate students, innovators, technologists, and industry professionals working in the areas of game theory and mechanism design, social networks, Internet and network economics, Multi-agent Learning, Multi-Armed Bandits, and Computer Science & Engineering in general. We would like to ensure that the tutorial is self-contained. It doesn’t assume any specific expertise from the audience. 

This lecture expects the attendees understand the following technical aspects:
 
o Foundational concepts in network science, game theory and mechanism design,
 
o Need for a game theoretic approach to deal with strategic and economic aspects of social networks,
 
o Online learning in multi-agent network systems (including the settings wherein the agents exhibit strategic behavior), and
 
o Design of computationally efficient algorithms for various important problems in network science.

They would have an appreciation of how game theory enables important problems to be solved. Accordingly, they could understand how the game theoretic approach will supplement and complement other classical approaches to social network analysis. Further, the attendees would also be able to appreciate use of online learning models in the presence of strategic agents in multi-agent network systems. Since it discusses various challenges and open problems at the end of this lecture, this surely helps researchers to push the boundaries of literature in this field. As the audience are expected to understand the foundational concepts in game theory, mechanism design, and online learning in multi-agent network systems in this lecture, they can apply these concepts in other application areas of their interest.

About this Lecture

Number of Slides:  120
Duration:  120 minutes
Languages Available:  English
Last Updated: 

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