To account for strong ageing characteristics of citation networks, we modify the PageRank algorithm by initially distributing random surfers exponentially with age, in favour 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 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 between 1893 and 2003 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. The advantages and performance of CiteRank over more conventional methods of ranking publications are discussed.

@article{1742-5468-2007-06-P06010, author = {Walker, Dylan and Xie, Huafeng and Yan, Koon-Kiu and Maslov, Sergei}, title = {Ranking scientific publications using a model of network traffic}, journal = {Journal of Statistical Mechanics: Theory and Experiment}, year = {2007}, volume = {2007}, number = {06}, pages = {P06010}, url = {http://stacks.iop.org/1742-5468/2007/i=06/a=P06010}, keywords = {paper, ranking, network, publication, toread, scientific}, abstract = {To account for strong ageing characteristics of citation networks, we modify the PageRank algorithm by initially distributing random surfers exponentially with age, in favour 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 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 between 1893 and 2003 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. The advantages and performance of CiteRank over more conventional methods of ranking publications are discussed.} }

%0 = article %A = Walker, Dylan and Xie, Huafeng and Yan, Koon-Kiu and Maslov, Sergei %D = 2007 %T = Ranking scientific publications using a model of network traffic %U = http://stacks.iop.org/1742-5468/2007/i=06/a=P06010