Use of Rapid Probabilistic Argumentation for Ranking on Large Complex Networks
| dc.creator | Cetin, Burak | |
| dc.creator | Bingol, Haluk | |
| dc.date | 2008-02-22 | |
| dc.date.accessioned | 2026-07-07T09:22:46Z | |
| dc.date.available | 2026-07-07T09:22:46Z | |
| dc.description | 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. | |
| dc.description | 11 pages, 10 figures | |
| dc.identifier | https://arxiv.org/abs/0802.3293 | |
| dc.identifier | http://arxiv.org/abs/0802.3293 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/155492 | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Information Retrieval | |
| dc.title | Use of Rapid Probabilistic Argumentation for Ranking on Large Complex Networks | |
| dc.type | text |