  We introduce a family of novel ranking algorithms called ERank which run in
linear/near linear time and build on explicitly modeling a network as uncertain
evidence. The model uses Probabilistic Argumentation Systems (PAS) which are a
combination of probability theory and propositional logic, and also a special
case of Dempster-Shafer Theory of Evidence. ERank rapidly generates approximate
results for the NP-complete problem involved enabling the use of the technique
in large networks. We use a previously introduced PAS model for citation
networks generalizing it for all networks. We propose a statistical test to be
used for comparing the performances of different ranking algorithms based on a
clustering validity test. Our experimentation using this test on a real-world
network shows ERank to have the best performance in comparison to well-known
algorithms including PageRank, closeness, and betweenness.
