Percolation Thresholds of Updated Posteriors for Tracking Causal Markov Processes in Complex Networks

Details Percolation Thresholds of Updated Posteriors for Tracking Causal Markov Processes in Complex Networks Percolation Thresholds of Updated Posteriors for Tracking Causal Markov Processes in Complex Networks

2009-05-14

arxiv.org/abs/0905.2236v1

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Machine Learning

Scientific paper

Percolation on complex networks has been used to study computer viruses, epidemics, and other casual processes. Here, we present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of node states resulting from actively monitoring the network. Since traditional percolation thresholds are derived using observation independent Markov chains, the threshold of the posterior should more accurately model the true phase transition of a network, as the updated posterior more accurately tracks the process. These conditions should provide insight into modeling the dynamic response of the updated posterior to active intervention and control policies while monitoring large complex networks.

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