How do human sensors interact over a social network such as YouTube or Twitter? The information structures in social sensing are very different compared to standard signal processing: the agents interact and influence each other. Such correlated behavior is affected by the structure of the social network. Our long term vision is to use behavioral economics with statistical signal processing to understand the interactive social sensing.
Book and Related Papers
- [Book]: V. Krishnamurthy, O. Namvar, M. Hamdi, Interactive Sensing and Decision Making in Social Networks, Foundations & Trends in Signal Processing, 2014. (click on the book icon on the right to access the entire book on arXiv).
- B. Nettasinghe, V. Krishnamurthy, What do your friends think? Efficient Polling Methods for Networks using Friendship Paradox, arXiv, 2018
- V. Krishnamurthy, S. Bhatt, T. Pedersen, Tracking Infection Diffusion in Social Networks: Filtering Algorithms and Threshold Bounds, IEEE Transactions on Signal and Information Processing over Networks, pp.298-315, June 2017.
- W. Hoiles, A. Aprem, V. Krishnamurthy, Engagement and Popularity dynamics of YouTube Videos and sensitivity to Meta-Data, IEEE Transactions Knowledge and Data Engineering, pp.1426-1437, 2017.
- S. Tanzil, W. Hoiles, V. Krishnamurthy, Adaptive Scheme for Caching YouTube content in cellular network: machine learning approach, IEEE Access Journal, Feb 2017.
O. Namvar, V.Krishnamurthy, G, Yin, Adaptive Search Algorithms for Discrete Stochastic Optimization: A Smooth Best-Response Approach, IEEE Transactions Automatic Control, 2017.
- V. Krishnamurthy, S. Bhatt, Sequential Detection of Market Shocks with Risk-Averse CVaR social sensors, IEEE Journal Selected Topics Signal Processing, 2016.
- V. Krishnamurthy, E Leoff, J. Sass, Filterbased stochastic volatility in continuous-time hidden Markov models, Econometrics and Statistics, Nov 2016.
- Krishnamurthy, W. Hoiles, Online Reputation and Polling Systems: Data Incest, Social Learning and Revealed Preferences, IEEE Transactions Computational Social Sciences, Jan. 2015.
- Hamdi, V. Krishnamurthy, G. Yin, Tracking a Markov-Modulated Stationary Degree Distribution of a Dynamic Random Graph, IEEE Transactions Information Theory, 2014.
- Hoiles, O. Namvar, V. Krishnamurthy, N. Dao and H. Zhang, Adaptive Caching in the YouTube Content Distribution Network: A Revealed Preference Game-Theoretic Learning Approach, IEEE Transactions on Cognitive Communications and Networking, 2015. (invited)
- Krishnamurthy and H.V. Poor, A Tutorial on Interactive Sensing in Social Networks, IEEE Transactions Computational Social Systems, Vol.1, No.1, (Inaugural Issue), 2014.
- Namvar, V. Krishnamurthy and G. Yin, Distributed Tracking of Correlated -Equilibria in Regime Switching Noncooperative Games, IEEE Transactions Automatic Control, pp.2435–2450, 2013.
O. Namvar. V. Krishnamurthy and G. Yin, Distributed Energy-Aware Diffusion Least Mean Squares: Game-Theoretic Learning, IEEE Journal Selected Topics in Signal Processing, Vol.7, No.5, pp.821–836, 2013.
- Krishnamurthy, Quickest Time Detection with Social Learning: Interaction of Local and Global Decision Makers, IEEE Transactions Information Theory, 2012.
V. Krishnamurthy, K. Topley and G. Yin, Consensus Formation in a two-time-scale Markovian System, SIAM Journal Multiscale Modeling and Simulation, Vol.7, No.4, pp.1898–1927, 2009.