Use of Rapid Probabilistic Argumentation for Ranking on Large Complex Networks

Details Use of Rapid Probabilistic Argumentation for Ranking on Large Complex Networks Use of Rapid Probabilistic Argumentation for Ranking on Large Complex Networks

2008-02-22

arxiv.org/abs/0802.3293v1

Computer Science

Artificial Intelligence

11 pages, 10 figures

Scientific paper

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.

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