Active Sensing of Social Networks
This work addresses the challenge of inferring social network dynamics for researchers in network analysis, though it is incremental as it builds on existing DeGroot models.
The paper tackles the problem of estimating trust weights in social networks by developing an active sensing method based on the DeGroot model with stubborn agents, proving that network structure can be revealed with sufficient stubborn agents and supporting this with simulation results on synthetic and real-world networks.
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.