# SentenceLDA- and ConNetClus-Based Heterogeneous Academic Network Analysis for Publication Ranking
## 1 Introduction  
Scientific papers, as the carrier of knowledge dissemination, play the role of \"standing on the shoulders of giants\" [1] With the development of Internet technology, the increase in scientific papers being published in the digital form not only helps avoid resource waste caused by the printing process but also promotes the speed and scope of knowledge dissemination. However, the rapid development trend of digital papers has introduced new challenges for scientific resources management. One estimate puts the count at 1.8 million articles published each year in about 28,000 journals [2] Digitally published articles foster more opportunities for information retrieval but make it harder to find the appropriate resource. Therefore, more and more people have focused on the methodology of evaluating scientific papers to improve the efficiency of information retrieval, eliminate the influence of non-relevant results, and satisfy the user's information needs. Fortunately, research on a publication ranking algorithm is one of the effective ways to solve this problem.  
In fact, many studies have focused on methods of scientific publication ranking. Citation counts [3] are the most popular method (that is, the ranking result is determined by the count of citations). This method assumes that the more citations a paper has, the higher its influence tends to be; thus, the paper is ranked higher. In addition, there is one publication ranking method based on the impact factors [4] of the venue or a paper published in a journal or a conference. If the venue has a higher impact factor, the paper published in it will also have a high influence. Besides that, the author's reputation will also determine the ranking result; that is, a paper written by well-known scholars in thefield is often ranked higher than others [5] Currently, the methods mentioned above are widely used in academic paper retrieval institutions or search engines as the basic or core algorithm for scientific paper searching. However, the above methods also have limitations when used for evaluating publications. For example, self-citation may confuse the result of citation counts, which affects the evaluation of author authority. Furthermore, the recognition of the venue affects the evaluation results of scientific papers.  
Fortunately, more and more methods (e.g., machine learning, text mining, network and graph, etc.) are used in bibliometrics for ranking. PageRank [6] algorithms rely on graph theory, which promotes the research of ranking and evaluating scientific papers. However, most methods only adopt a single type of network structure and neglect the influence of different types of vertexes for ranking. The single type of network structure may include a publication network by citation relationship, keyword network by co-occurrence relationship, author network by co-author relationship, etc.  
Therefore, in this paper, we propose a publication ranking method based on heterogeneous academic network analysis. The metadata that affect the evaluation of scientific papers are regarded as the vertexes in the heterogeneous network (i.e., title, topic, keyword, author, venue, abstract and full text, etc.). We then use the SentenceLDA algorithm for training topics by the metadata of the abstract, and the Gibbs sampling method is proposed for creating the heterogeneous network (also known as TVKA). Finally, we apply the ConNetClus algorithm to calculate the probability value of publication ranking.  
As one contribution of this paper, we propose a method for creating a heterogeneous academic network, which is topic-sensitive with four types of vertexes. The other contribution is that we use the ConNetClus algorithm to calculate the probability value in the heterogeneous network as the publication ranking result, which we compare with the classical ranking algorithm. As the result shows, the method we use in this paper is better than other baselines.