Anand Kalvit
ABSTRACT
In this work, we examine the problem of rumor source inference on a network whose topology is known, given infected nodes and pairwise information in the form of pairwise partial orders on the set of nodes of the underlying graph based on the order in which they were infected. We analyze the Maximum Likelihood Estimator (MLE) of the rumor source, assumed unique, and compare it with other estimators popular in literature, e.g., rumor center, distance center and Jordan center. We propose an approximation to the MLE and a class of estimators based on this approximation that is agnostic to the underlying rumor model. We also propose MCMC algorithms to implement them. Further, we assess the robustness of the proposed estimators to different graph topologies via extensive simulations on the Erdős-Rényi and Barabási-Albert models.
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Index Terms
- MCMC Approaches to Rumor Source Inference using Pairwise Information
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