Co-Ranking Authors and Documents in a Heterogeneous Network

The problem of evaluating scientific publications and their authors is important, and as such has attracted increasing attention. Recent graph-theoretic ranking approaches have demonstrated remarkable successes, but most of their applications are limited to homogeneous networks such as the network of citations between publications. This paper proposes a novel method for co-ranking authors and their publications using several networks: the social network connecting the authors, the citation network connecting the publications, as well as the authorship network that ties the previous two together. The new coranking framework is based on coupling two random walks, that separately rank authors and documents following the PageRank paradigm. As a result, improved rankings of documents and their authors depend on each other in a mutually reinforcing way, thus taking advantage of the additional information implicit in the heterogeneous network of authors and documents. The proposed ranking approach has been tested using data collected from CiteSeer, and demonstrates a great improvement in author ranking quality compared with ranking by the number of publications, the number of citations and the PageRank calculated in the authors’ social network