# SentenceLDA- and ConNetClus-Based Heterogeneous Academic Network Analysis for Publication Ranking
## 3 Methodology
### Ranking Algorithm for Heterogeneous Academic Networks  
The NetClus algorithm is a ranking algorithm suitable for star heterogeneous networks; it divides different types of vertexes into attribute and target objects and uses the association between different types of vertexes in the clustering ranking process. In a star heterogeneous academic network, papers are defined as target objects, and authors, venues, and keywords are defined as attribute objects. During this operation, the NetClus algorithm uses the relationship between papers and authors, the relationship between papers and venues but ignores the citation relationship between papers and papers. The citation relationship between papers is equally important for heterogeneous academic networks. Therefore, this paper hopes to use the relationship between papers and papers and the relationship between papers and the attribute types of authors, keywords, and venues. Therefore, the ConNetClus algorithm optimized by the NetClus algorithm is adopted. A sample data relationship in this paper Is shown in Figure 4. Suppose there are four papers (\(D\)) as the target type, and there is a citation relationship between the papers; meanwhile, the papers have a link relationship with the attribute types of author (\(A\)), keyword (\(T\)), and venue (\(C\)), respectively. The ConNetClus algorithm framework is as follows:  
1 The target object is initially divided into \(K\) clusters, and the initial network clustering is generated according to the partition, that is \(\left\{G_{k}^{t}\right\}_{k=1}^{K}\). The superscript 0 represents the 0 th round, and \(G_{k}^{0}\) represents the target object included in the initialization cluster \(k\). 2 Calculate a ranking-based probabilistic generative model for each target object under each cluster, \(\left\{P\left(x|G_{k}^{t}\right)\right\}_{k=1}^{K}\). 3 Calculate the posterior probability \(P\left(G_{k}^{t}|x\right)\) of each target object under each cluster and adjust the cluster assignment result according to the evaluation of the posterior probability. 4 Repeat steps 2 and 3 until all clustering results no longer change significantly. That is, \(\left\{G_{k}^{t}\right\}_{k=1}^{K}=\left\{G_{k}^{t-1}\right\}_{k=1}^{K}\). 5 After the results are obtained for the target objects, the probability distributions of different types of attribute objects are obtained in each cluster so as to obtain the sorting results. Generating probability model of target and attribute objects. To simplify different types of objects in heterogeneous networks, different types of objects are decomposed, and then the generation behavior of the paper of the target object is modeled. If the probability of the authors, journals, and keywords of the paper being accessed is high, or the probability of the papers cited in the paper being accessed is high, it can be considered that the paper is highly likely to be accessed. The idea of decomposing heterogeneous networks is that for a heterogeneous network \(G\), the access probabilities of objects of different attribute types are independent of each other. For example, author \(a_{i}\), \(p(a_{i}|G)\) can be decomposed into two parts: \(p(a_{i}|G)=p(A|G)\times p(a_{i}|A,G)\), where \(p(A|G)\) indicates the type of access author and \(p(a_{i}|A,G)\) represents the probability of facing all authors but accessing object \(a_{i}\). Therefore, for an attribute object \(x\) and its type \(T_{x}\), the probability of accessing \(x\) in network \(G\) is defined as follows:  
\[p(x|G)=p(T_{x}|G)\times p(x|T_{x},G) \tag{10}\]  
Figure 4: Star heterogeneous academic network. We stop the iteration when the target object in the cluster no longer changes. Finally, we have the result of comprehensive sorting. After the clustering and ranking of ConNetClus, the clustered classifiers and sorting results of each type of vertex are obtained. Then, we use the word similarity of Wikipedia [41] to calculate the similarity between the clustered classifiers and words under the topic calculated.