Ranking Scientific Publications Using a Simple Model of Network Traffic Export

@article{citerank, abstract = {To account for strong aging characteristics of citation networks, we modify Google's PageRank algorithm by initially distributing random surfers exponentially with age, in favor of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We develop an analytical understanding of traffic flow in terms of an RPA-like model and optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar.}, archivePrefix = {arXiv}, author = {Walker, Dylan and Xie, Huafeng and Yan, Koon-Kiu and Maslov, Sergei}, citeulike-article-id = {1189220}, citeulike-linkout-0 = {http://arxiv.org/abs/physics/0612122}, citeulike-linkout-1 = {http://arxiv.org/pdf/physics/0612122}, eprint = {physics/0612122}, month = {Dec}, posted-at = {2008-06-23 18:24:40}, priority = {0}, title = {Ranking Scientific Publications Using a Simple Model of Network Traffic}, url = {http://arxiv.org/abs/physics/0612122}, year = {2006} }

TY - JOUR ID - citerank L3 - citeulike-article-id:1189220 N2 - To account for strong aging characteristics of citation networks, we modify Google's PageRank algorithm by initially distributing random surfers exponentially with age, in favor of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We develop an analytical understanding of traffic flow in terms of an RPA-like model and optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar. TI - Ranking Scientific Publications Using a Simple Model of Network Traffic AU - Walker, Dylan AU - Xie, Huafeng AU - Yan, Koon-Kiu AU - Maslov, Sergei PY - 2006/Dec/13/ UR - http://arxiv.org/abs/physics/0612122 ER -