# SentenceLDA- and ConNetClus-Based Heterogeneous Academic Network Analysis for Publication Ranking
## 4 Experiments
### Evaluation Results  
To verify the influence of the heterogeneous academic network analysis for the academic paper ranking algorithm, this paper first compares the method proposed in this paper with the classical algorithm. PLSA is the probabilistic latent semantic analysis model. Its main goal is to reduce the dimension and map the document from the sparse, high-dimensional word space to low-dimensional vector space. This method can create a model by a single vertex as publication, author, journal, or keyword, separately, and cannot build a heterogeneous network. Therefore, PLSA was compared with the method proposed in this paper (SenLDA + ConNetClus) to verify if there are advantages to heterogeneous network analysis. BM25 is a classical language model method of information retrieval, which is realized by calculating the correlation between network objects and query keywords. BM25 +ConNetClus is also compared to test if the SenLDA + ConNetClus method is better than the language model for publication ranking. PageRank is a global page ranking algorithm based on the intuition that links from important pages are more significant than links from other pages. This method has been widely used in citation analysis for evaluating the importance of vertexes in the network; however, it is independent of topics. Therefore, LDA + PageRank, as used in this paper, is tested to prove that the SenLDA + ConNetClus method is more significant in heterogeneous academic network computing and analysis. The ground truth in this paper is from the manual annotation of physics students and gives the ranking results for papers, venues, keywords, and authors in a specific topic. Considering that users may pay more attention to the top results in information retrieval, the labeling process takes 5 results as the standard. Then, using the search intersection of the algorithm proposed in this paper, BM25 and hep-th system, the search words are selected for experimental comparison. The evaluation results in MAP and NDCG are shown in Tables 3 and 4. From the experimental results of MAP shown in Table 3 and Figure 6, it can be found that the higher the MAP value, the more similar the ranking result is close to the ground truth. It can be clearly seen in Figure 5 that the senLDA + ConNetClus method proposed in this paper obtains better calculation results, and its MAP value is higher than other algorithms, in MAP@10, @50, and @100. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Object** & **NDCG@10** & **@50** & **@100** & **Method** & **Object** & **NDCG@10** & **@50** & **@100** \\ \hline \multirow{4}{*}{BM25} & Publication & 0.067 & 0.083 & 0.097 & \multirow{4}{*}{BM25 + ConfNetClus} & Publication & 0.068 & 0.083 & 0.096 \\ \cline{2-10}  & Venue & 0.041 & 0.062 & 0.071 & & \multirow{4}{*}{ConNetClus} & & Venue & 0.052 & 0.071 & 0.087 \\ \cline{2-10}  & Keyword & 0.083 & 0.104 & 0.11 & & & & & & & & & & & \\ \cline{2-10}  & Author & 0.088 & 0.103 & 0.119 & & & & & & & & & \\ \cline{2-10}  & AVE. & 0.0698 & 0.088 & 0.0993 & & & & & & & & & \\ \hline LDA + PageRank & Publication & 0.088 & 0.097 & 0.118 & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{PLSA} & Publication & 0.071 & 0.086 & 0.1 & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of NDCG.