# Citation Oriented AuthorRank for Scientific Publication Ranking
## 1 Introduction  
With the rapid increase in online scientific literature, the task of publication ranking has become increasingly important for scholarly information retrieval and recommendation, which aims at enhancing the efficiency of research. Generally speaking, the article with more influence should be ranked higher in the publication ranking. While citation analysis plus graph mining is a commonly used bibliometric method of performing this task. As contrasted with the traditional method of simple citation counting [1], PageRank [2] is a global page ranking algorithm based on the intuition that links from important pages are more significant than links from other pages. It is an effective means of ranking web pages and scientific publications for addressing user information needs. The relative \"importance\" of a node is calculated by back-links with transition authority from other nodes, thereby determining which nodes are more important in the network.  
However, the original PageRank algorithm is based solely on links, independent of any particular search query, and regardless of other metadata in the content, e.g., title, keywords, abstract, and link location. Because of this limitation, some previous studies have tried to improve PageRank by employing topic-sensitive methods [3], which take the topic into account in determining the rank. Then, it can be widely used for discovering important papers [4].  
As one contribution of this paper, we used an optimized topic-sensitive PageRank algorithm, PageRank with Priors (PRP), to determine the topical ranking scores of scientific publications and authors. This method has been proposed in our previous work [5],which combined PageRank and a supervised topic modeling algorithm Labeled-LDA [6] via full-text extraction with two priors: a publication topic probability and a citation transition probability, and, then, publication ranking results and author ranking results were presented by the PRP algorithm.  
On the other hand, the idea behind AuthorRank [7] is that content created by more popular authors should rank higher than content created by less popular authors. It was proposed by Google's Agent Rank patent in 2005, but cannot be implemented in ranking web pages, because it is difficult and expensive to assess the topical reputation or importance of many Web page creators. Actually, it is much easier and economical in the scientific literature domain. For scientific literature, the creator, as an author, is a common and important metadata item, easy to extract from a digital library, and widely studied in previous research [8]. So, the other contribution of this paper is to generate a topical AuthorRank score, which is based on full-text extraction and the Labeled-LDA model, according to topic-sensitive author ranking results by four methods: first author, last author, the most famous author, and average author.  
Finally, to test whether topical AuthorRank can replace PageRank, or how it can make the results more accurate for publication ranking [9], we will compare publication ranking results by using topical AuthorRank plus PRP to validate the results. In order to confirm that AuthorRank positively influences publication ranking, two combination methods will be validated, the linear method and the Cobb-Douglas method.  
In the remainder of this paper, we: (1) review relevant literature on the methodology of publication ranking and AuthorRank; (2) introduce our novel methods for publication and AuthorRank, as well as how to achieve publication ranking with AuthorRank; (3) describe the experimental setting and evaluation results; and (4) discuss the findings and limitations of the study and identify subsequent research steps.