# Citation Oriented AuthorRank for Scientific Publication Ranking
## 4 Experiments
### Experimental Result  
As proposed in the section on methods, the PRP combined Labeled-LDA topic model with full-text citation analysis measured the relative importance of vertices (papers) in the publication networks. The vertices (41,370 publications) and edges (223,810 citations) represented a topic distribution on 3910 topics. For each topic, the graph may be totally different than for others.  
In Figure 2, the first graph is the complete graph with 41,370 vertices and 223,810 edges, while the second one is a sub-graph with 580 vertices and 1148 edges based on the topic \"Information Retrieval\" (i.e., the publications used \"Information Retrieval\" as a keyword  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **References Format** \\ \hline
1 &...... [num1]...... \\
2 &...... [num1,num2]...... \\
3 &...... [num1-num2]...... \\
4 &...... [num1, num2-num3]...... \\ \hline \hline \end{tabular}
\end{table}
Table 3: Format of references in publications.  
or \"Information Retrieval\" was found in the paper abstract by using greedy matching). The last graph with 3356 vertices and 6671 edges is an extended graph of the \"Information Retrieval\" graph, which means each node on the graph is directly or conditionally (cited by a directly relevant paper) related to \"information retrieval\". The biggest node in these two graphs is \"R_291008: A language modeling approach to information retrieval\", which means that the citation count is the biggest one in our dataset.  
In order to compare the different methods for publication ranking results, we took the topic \"Information Retrieval\" as our example and listed all the results by seven methods, as shown in Table 4 (top five results shown). PageRank is the original PageRank algorithm, where the damping factor is 0.85. PRP_1 is the PageRank with priors algorithm with only one prior: publication topic probability. PRP_1 neglects the other prior, the citation transition probability, thereby treating the scores of all the edges as equal. PRP_2 brings two kinds of prior knowledge into the citation graph by LLDA: publication topic probability and citation transition probability. First Author, Last Author, Max Author, and Average Author were proposed before as methods for calculating an AuthorRank.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ranking Results** & **PageRank** & **PRP\_1** & **PRP\_2** & **First Author** & **Last Author** & **Max Author** & **Average Author** \\ \hline Top1 & R\_359342 & R\_321930 & R\_951766 & R\_321035 & R\_363817 & R\_363817 & R\_321084 \\ Top2 & R\_362685 & R\_321757 & R\_321930 & R\_321084 & R\_3197 & R\_3197 & R\_321035 \\ Top3 & R\_805047 & R\_358466 & R\_321035 & R\_511295 & R\_321084 & R\_321035 & R\_511295 \\ Top4 & R\_805694 & R\_321035 & R\_358466 & R\_122864 & R\_322053 & R\_321084 & R\_363817 \\ Top5 & R\_805695 & R\_951766 & R\_321074 & R\_1095451 & R\_321035 & R\_322053 & R\_109545 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Publication ranking results.  
Figure 2: Publication graphs: (**a**) Visualization of complete graph (41,370 vertices and 223,810 edges). (**b**) Visualization of sub-graph on “Information Retrieval” (580 vertices and 1148 edges). (**c**) Visualization of extend graph on “Information Retrieval” (3356 vertices and 6671 edges).  
In this table, we found that ranking result lists are totally different by different methods. For example, the paper \"R_321035: On Relevance, Probabilistic Indexing and Information Retrieval\" authored by [PERSON] and published in the \"Journal of the ACM\" in 1960, was ranked high according to both PageRank and AuthorRank, but there also exist papers with high AuthorRank but low PageRank or, conversely, low AuthorRank and high PageRank. The former may indicate a new publication resource, one the importance of which has not yet been widely recognized. The latter might be an important paper by a young or relatively unknown author.