# SentenceLDA- and ConNetClus-Based Heterogeneous Academic Network Analysis for Publication Ranking
## 3 Methodology
### Construction of the Heterogeneous Academic Network

Table 1: The notations and their interpretations. In order to better explain this question, Figure 2 shows the process of a heterogeneous network disassembling into several homogeneous sub-networks. The heterogeneous network has four types of vertexes, so we can obtain four sub-networks according to the relationships of edges and vertexes. In this paper, the reason for designing the heterogeneous academic network is based on users' requirements for academic information. This means users are not only eager to obtain the ranking of papers but also want to understand the influence of related topics, the authority of authors, the impact factor of venues, as well as the highly relevant topics with search terms. Considering the internal and external factors of scientific publication evaluation, we construct a heterogeneous academic network. The external factors include venue impact, author authority, citation relationship, and co-author. The internal factors include the title, abstract, and keyword. Because there are certain dependencies between these metadata, and there is a one-to-one relationship between title and abstract, we convert the metadata into topics for analysis in processing. Therefore, the heterogeneous academic network in this paper has four types of vertexes (i.e., publication (title), venue, author, and topic). The types of edges include publication to publication (created by a citation relationship), author to author (created by a co-author relationship), topic to topic (created by a co-occurrence relationship), publication to venue (the paper is published in the venue), publication to author (the paper is created by an author), and publication to topic (the paper includes a topic). The list is shown in Table 2. Considering that this paper aims to propose an algorithm for publication ranking, the vertex in the network is mainly about publications. The co-author relationship and keyword co-occurrence relationship are ignored in the latter part of this paper. Figure 1: A simple example of the heterogeneous network. Figure 2: Sub-networks disassembled from a heterogeneous network. We also take an example in Figure 3 to illustrate the heterogeneous academic network. Author \(a_{1}\) writes two papers, \(d_{1}\) and \(d_{2}\). Paper \(d_{2}\) is cited by paper \(d_{1}\). The topic \(t_{1}\) is included in \(d_{1}\), and topic \(t_{2}\) is included in \(d_{2}\). \(d_{1}\) and \(d_{2}\) are published in venue \(c_{1}\) and \(c_{2}\), respectively. Paper \(d_{3}\) is written by author \(a_{2}\) with the topic \(t_{3}\) and published in venue \(c_{2}\). Author \(a_{3}\) and author \(a_{4}\) jointly complete paper \(d_{4}\), which includes the topics \(t_{3}\) and \(t_{4}\). Paper \(d_{4}\) is published in venue \(c_{3}\). Paper \(d_{3}\) is cited by papers \(d_{2}\) and \(d_{4}\). Paper \(d_{5}\) is created by author \(a_{4}\), and published in venue \(c_{4}\). By considering the correlation of the heterogeneous network, this computing structure offsets the limitations of the homogeneous network. When constructing the heterogeneous academic network, we introduced the Gibbs sampling model to represent the topic relevance. When different authors work together to complete a paper, they may contribute to different topics of the paper. At the same time, the topic of this paper also determines which journals the paper can be submitted to and which topics are relevant. In this model, the generation of topics is determined by the words. Therefore, we can extract an author from all authors and determine the topic generated by that author, that is, the implicit distribution of the topic. Then, we extract a topic from the probability distributions of all words and obtain the topic of venues and keywords from the distributions of all journals and keywords. The overall process can be divided into the following steps:  
1.