2 research outputs found
Detecting Communities in a Gossip Model with Stubborn Agents
We consider a community detection problem for a gossip model, in which agents
randomly interact pairwise, and there are stubborn agents never changing their
states. Such a model can illustrate how disagreement and opinion fluctuation
arise in a social network. It is assumed that each agent is assigned with one
of the two community labels, and the agents interact with probabilities
depending on their labels. The considered problem is twofold: to infer the
community labels of agents, and to estimate interaction probabilities between
the agents, based on a single trajectory of the model. We first study stability
and limit theorems of the model, and then propose a joint detection and
estimation algorithm based on agent states. It is verified that the community
detector of the algorithm converges in finite time, and the interaction
estimator converges almost surely. We derive a sample-complexity result for
successful community detection, and analyze convergence rate of the interaction
estimator. Simulations are presented for illustration of the performance of the
proposed algorithm