Ranking Authors in Citation Networks with Automated Keyword Extraction using Word Embeddings

The main objective of this present article is to propose an efficient way to evaluate and rank the researchers and academic authors by using citation network dataset. To rank the authors in the citation network to predict the prominent authors in the research domain for making ease for authors for identifying the predominant authors in their area of research work. Popular independent metrics like h-index, number of citations, and so on is not very reliable when it comes to ranking authors. Author come up with a new method called Citation enhanced Ranking of Authors (CeRA) to rank authors. The proposed approach CeRA approach utilizes content-related similarity between abstracts that extract the keywords by using the word2vec model. K-means clustering algorithm is applied to extract authors clusters based on domains. Finally, specific authors rank is retrieved using the page rank of the author's node in a cluster.