Discovering influential authors in heterogeneous academic networks by a co-ranking method

Abstract

Research in ranking networked entities is widely applicable to many problems such as optimizing search engines, building recommendation systems and discovering influential nodes in social networks. However, many famous ranking approaches like PageRank are limited to solving this problem in homogeneous networks and are not applicable to heterogeneous networks. Faced with this problem, we propose a co--ranking method to evaluate scientific publications and authors. This novel approach is a flexible framework based on a set of customized rules taking into account both topological features of networks and the included citations. The approach ranks authors and publications iteratively and uses the results of each round to reinforce the ranks of authors and publications. Unlike traditional approaches to assessing publication, which require a great number of citations, our method lowers this requirement. This co--ranking approach has been validated using data collected from DBLP and CiteSeer, and the results suggest that it is effective and efficient in ranking authors and publications based on limited numbers of citations in heterogeneous networks and that it has fast convergence.

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Papers that cite the same references — the relational fallback while embeddings are still backfilling.