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Practical significance spans from graph theory to interdisciplinary fields like biology, sociology, economics, and marketing. Despite rich literature in this direction, we find small notable effort to consistently compare and rank existing centralities considering both the topology and the opinion diffusion model, as well as considering the context of <jats:italic>simultaneous<\/jats:italic> spreading. To this end, our study introduces a new benchmarking framework targeting the scenario of <jats:italic>competitive opinion diffusion<\/jats:italic>; our method differs from classic SIR epidemic diffusion, by employing competition\u2010based spreading supported by the realistic tolerance\u2010based diffusion model. We review a wide range of state\u2010of\u2010the\u2010art node ranking methods and apply our novel method on large synthetic and real\u2010world datasets. Simulations show that our methodology offers much higher quantitative differentiation between ranking methods on the same dataset and notably high granularity for a ranking method over different datasets. We are able to pinpoint\u2014with consistency\u2014which influence the ranking method performs better against the other one, on a given complex network topology. We consider that our framework can offer a forward leap when analysing diffusion characterized by real\u2010time competition between agents. These results can greatly benefit the tackling of social unrest, rumour spreading, political manipulation, and other vital and challenging applications in social network analysis.<\/jats:p>","DOI":"10.1155\/2018\/4562609","type":"journal-article","created":{"date-parts":[[2018,8,16]],"date-time":"2018-08-16T23:30:49Z","timestamp":1534462249000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Competition\u2010Based Benchmarking of Influence Ranking Methods in Social Networks"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7244-0732","authenticated-orcid":false,"given":"Alexandru","family":"Top\u00eerceanu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,8,16]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature02541"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0701361104"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1420068112"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.84.036106"},{"key":"e_1_2_9_5_2","doi-asserted-by":"crossref","unstructured":"PrakashB. 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