A tutorial on interactive sensing in social networks

Vikram Krishnamurthy, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


This paper considers models and algorithms for interactive sensing in social networks in which individuals act as sensors and the information exchange between individuals is exploited to optimize sensing. Social learning is used to model the interaction between individuals that aim to estimate an underlying state of nature. In this context, the following questions are addressed: how can self-interested agents that interact via social learning achieve a tradeoff between individual privacy and reputation of the social group? How can protocols be designed to prevent data incest in online reputation blogs where individuals make recommendations? How can sensing by individuals that interact with each other be used by a global decision maker to detect changes in the underlying state of nature? When individual agents possess limited sensing, computation, and communication capabilities, can a network of agents achieve sophisticated global behavior? Social and game-theoretic learning are natural settings for addressing these questions. This article presents an overview, insights, and discussion of social learning models in the context of data incest propagation, change detection, and coordination of decision-making.

Original languageEnglish (US)
Article number6780648
Pages (from-to)3-21
Number of pages19
JournalIEEE Transactions on Computational Social Systems
Issue number1
StatePublished - Mar 1 2014

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction


  • Coordination
  • correlated equilibria
  • data incest
  • game-theoretic learning
  • information diffusion
  • reputation systems
  • social learning
  • social sampling


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