The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Stable algorithms for link analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Assessing the relative influence of journals in a citation network
Communications of the ACM
Co-ranking Authors and Documents in a Heterogeneous Network
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Time Sensitive Ranking with Application to Publication Search
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Applying centrality measures to impact analysis: A coauthorship network analysis
Journal of the American Society for Information Science and Technology
PageRank for ranking authors in co-citation networks
Journal of the American Society for Information Science and Technology
Topological centrality and its e-Science applications
Journal of the American Society for Information Science and Technology
P-Rank: An indicator measuring prestige in heterogeneous scholarly networks
Journal of the American Society for Information Science and Technology
Ranking authors in digital libraries
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Towards an effective and unbiased ranking of scientific literature through mutual reinforcement
Proceedings of the 21st ACM international conference on Information and knowledge management
Hi-index | 0.00 |
Scientific importance ranking has long been an important research topic in scientometrics. Many indices based on citation counts have been proposed. In recent years, several graph-based ranking algorithms have been studied and claimed to be reasonable and effective. However, most current researches fall short of a concrete view of what these graph-based ranking algorithms bring to bibliometric analysis. In this paper, we make a comparative study of state-of-the-art graph-based algorithms using the APS (American Physical Society) dataset. We focus on ranking researchers. Some interesting findings are made. Firstly, simple citation-based indices like citation count can return surprisingly better results than many cutting-edge graph-based ranking algorithms. Secondly, how we define researcher importance may have tremendous impacts on ranking performance. Thirdly, some ranking methods which at the first glance are totally different have high rank correlations. Finally, the data of which time period are chosen for ranking greatly influence ranking performance but still remains open for further study. We also try to give explanations to a large part of the above findings. The results of this study open a third eye on the current research status of bibliometric analysis.