# Citation Oriented AuthorRank for Scientific Publication Ranking
## 2 Previous Research  
### Publication Ranking  
Currently, rapid access to digital publications can greatly accelerate research, but the information overload also challenges researchers, especially junior researchers, in finding appropriate citations for their research projects. In previous papers, to deal with this problem, bibliometric methods have focused on ranking publications by citation analysis along with graph mining. A common and easy method based on citation frequency or citation impact has been studied for a long time. For example, the article [10] used citation analysis to assist college chemistry libraries with selecting significant books. In 1972, the volume of publications had increased dramatically, and with the emerging availability of electronic and online resources, it described a method of evaluating scientific journals by citation frequency and impact using data from digital resources [11]. More recently, citation information has been successfully used to enhance information retrieval performance [12].  
Nevertheless, various methods were based on a basic assumption, which is straightforward: if paper1 cites paper2, then paper1 and paper2 are related. This assumption is oversimplified because it treats all citations equally, regardless of sentiment, reason, topic, or motivation. Hence, some scholars have considered other factors bearing upon citation analysis results. Citation location was a significant factor [13], noting that a publication cited in the introduction or literature review section and mentioned again in the methodology or discussion sections is likely to make a greater overall contribution to the citing publication than others that have been mentioned only once. Further, citation context [14], a text window containing the target citation tag, may provide detailed and direct information about the nature of a citation, and also can be used to infer semantic information about the cited paper.  
The PageRank algorithm, first proposed and used in Google Search in 1998, is a significant method for evaluating node importance via complex graphs analysis, e.g., social networks, web graphs, telecommunication networks, and biological networks. However, PageRank is insensitive to topics and queries. [PERSON] [3] proposed a topic-sensitivePageRank algorithm, computing a set of PageRank vectors biased using a set of representative topics to capture more accurately the notion of importance with respect to a particular topic by computing topic-sensitive PageRank scores using the topic of the context in which the query appears, and then generating context-specific importance scores for pages using linear combinations of biased PageRank vectors. After that, more and more researchers have used PageRank or the improved PageRank algorithm in the field of bibliometrics for measuring publication and author importance [15; 16; 17].  
Currently, more and more scholars tried to optimize the PageRank by topic modeling. Latent Dirichlet Allocation (LDA) [18] is the most widely used method among them. Recent work shows that LDA topic modeling can be applied to scholarly network citation analysis. Labeled-LDA [6] is a supervised topic model that constrains LDA by defining a one-to-one correspondence between LDA's latent topics and user tags. The score calculated by topic modeling will incorporate prior probabilities as a relative-rank extension to PageRank [19]. Although some previous studies have used the topic model in conjunction with the PageRank algorithm [20], most only utilize the metadata of title, author, and abstract, but neglect full-text metadata.  
In this paper, we combined a Labeled-LDA topic model with PageRank via full-text extraction and incorporated two prior probabilities--publication topic probability and citation transition probability--for ranking the publications in our dataset.