| Each year, tens of thousands of scholarly journals publish hundreds of thousands of scholarly papers, collectively containing tens of millions of citations. As De Solla Price recognized in 1965, these citations form a vast network linking up the collective research output of the scholarly community. These well-defined and well-preserved networks are model systems well suited for studying communication networks and the flow of information on these networks. In this dissertation, I explain how I used citation networks to develop an algorithm that I call 'Eigenfactor.' The goal of Eigenfactor is to mine the wealth of information contained within the full structure of the scholarly web, in order to identify the important nodes in these networks. This is different from the conventional approach to scholarly evaluation. Metrics like impact factor ignore the network when ranking scholarly journals and only count incoming links. Eigenfactor not only counts citations but takes into account the source of those citations. By considering the whole network, I claim that Eigenfactor is a more information rich statistic. Librarians, publishers, editors and scholars around the world are now using Eigenfactor alongside impact factor to evaluate their journal collections. This dissertation consists of a collection of papers that provide an overview of Eigenfactor -- what it is, what it measures and how it can be used to better evaluate and navigate the ever-expanding scholarly literature. |
