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arXiv · 0802.3293

Use of Rapid Probabilistic Argumentation for Ranking on Large Complex Networks

Burak Cetin·Haluk Bingol
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Abstract

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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Burak Cetin, Haluk Bingol. 2008-02-22. Use of Rapid Probabilistic Argumentation for Ranking on Large Complex Networks. https://arxiv.org/abs/0802.3293

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