Active Sensing of Social Networks

Abstract

This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i.e., agents whose opinions are not influenced by their neighbors. This method can be viewed as a \emph{social RADAR}, where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents' opinions. The social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix. We prove that the network structure will be revealed when a sufficient number of stubborn agents independently influence a number of ordinary (non-stubborn) agents. We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states for the latter scenario. Simulation results on synthetic and real world networks support our findings.

Final version appeared in IEEE Transactions on Signal and Information Processing over Networks ( Volume: 2, Issue: 3, Sept. 2016 )

References in corpus (4)

• Revealing Network Connectivity From Dynamics
• Does a Single Zealot Affect an Infinite Group of Voters ?
• How does public opinion become extreme?
• The Social System Identification Problem

Cited by in corpus (18)

• A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part I
• Novel Multidimensional Models of Opinion Dynamics in Social Networks
• A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part II
• Recent advances in opinion propagation dynamics: A 2020 Survey
• Cyber-Social Systems: Modeling, Inference, and Optimal Design
• Exact Blind Community Detection from Signals on Multiple Graphs
• Randomized opinion dynamics over networks: influence estimation from partial observations
• Spectral partitioning of time-varying networks with unobserved edges
• Learning Quadratic Games on Networks
• Learning hidden influences in large-scale dynamical social networks: A data-driven sparsity-based approach
• Blind Community Detection from Low-rank Excitations of a Graph Filter
• Simultaneous Distributed Estimation and Attack Detection/Isolation in Social Networks: Structural Observability, Kronecker-Product Network, and Chi-Square Detector
• Community Structure Recovery and Interaction Probability Estimation for Gossip Opinion Dynamics
• A Two-Functional-Network Framework of Opinion Dynamics
• Recovering the Structural Observability of Composite Networks via Cartesian Product
• Evaluation and Control of Opinion Polarization and Disagreement: A Review
• On the Impact of Random Actions on Opinion Dynamics
• Estimating Centrality Blindly from Low-pass Filtered Graph Signals