Social networks offer users new means of accessing information, essentially relying on ``social filtering'', i.e. propagation and filtering of information by social contacts. The sheer amount of data flowing in these networks, combined with the limited budget of attention of each user, makes it difficult to ensure that social filtering brings relevant content to the interested users. I will present two perspectives on filtering, or curating, of relevant content under a limited budget of attention. First, I will consider how users may self-organize their connections to receive content of interest to them. To this end I will introduce flow games, a simple abstraction that models network formation under selfish user dynamics, featuring user-specific interests and budget of attention. Second, I will consider curating of a user's personal stream when sourced from aggregators. Here I will use the framework of utility games to demonstrate the efficiency of incentive-based curation mechanisms.
Nidhi Hegde is a senior researcher at Technicolor's Paris Research Center. She completed her B.Sc in Biochemistry at the University of Alberta, Canada in 1995, and received her M.S and Ph.D degrees in Computer Science from the University of Missouri-Kansas City in 1998 and 2000 respectively. She has worked at Bell Labs (New Jersey, USA) on wireless networks, at INRIA (Sophia-Antipolis, France), CWI and EURANDOM (The Netherlands) on models of performance analysis. She was a researcher at France Telecom R&D from 2005-2010 where she developed models for network dimensioning and wireless scheduling. She has been at Technicolor since 2010 and has been working on control in the smart grid, analysis of social networks for information dissemination and privacy-preserving mechanisms for user data.