Testing Ranking Algorithms on CiteSeer Data

Testing Ranking Algorithms on CiteSeer Data

This article describes how various ranking algorithms have been tested to evaluate researchers based on the data from a digital library called CiteSeer. We apply five well-known ranking methods such as citation counts, HITS, or PageRank and seven other methods derived from PageRank that take into account not only citation but also collaboration information to assess the importance of individual researchers. We compare the resulting rankings and show that some of them are highly correlated while others are not.
The available full text is a preprint of the article.

Keywords: CiteSeer, researchers, rankings, PageRank, correlation, convergence.

Year: 2013

Journal ISSN: 2277-6370
Download: download Full text [397 kB]

Authors of this publication:

Dalibor Fiala

Phone: +420 377 63 2429
E-mail: dalfia@kiv.zcu.cz
WWW: http://www.kiv.zcu.cz/~dalfia/

Dalibor is the research group coordinator and an associate professor at the Department of Computer Science and Engineering at the University of West Bohemia in Pilsen, Czech Republic. He is interested in data mining, web mining, information retrieval, informetrics, and information science.

Related Projects:


Social Networks Analysis

Authors:  Karel Je┼żek, Dalibor Fiala, Michal Nykl
Desc.:Application of the PageRank algorithm and its modifications to the exploration of network structures, particularly citation and co-autorship networks.