## 5 Evaluation
### Selection Results  
We used Lasso, Elastic Net, and OMP methods to filter out the top 10 factors of influence on publication from the 19 factors in the data (except for paper publication), respectively. In order to facilitate the next analysis and comparison, we adopted the interval equivalence division to divide the results into A, B, C, D four grades for ranking and mapped with different colors (A-red, B-green, C-orange and D-blue). As shown in Figure 3a, the 10 factors selected by each of the three algorithms are not identical. The ranking order of the factors selected by Lasso and Elastic Net is roughly the same, with the only difference from the order of Tguidance5 and Sacademic4 (Figure 3b). Both factors were ranked as D (mapping in blue) in the ranking process, so the difference in ranking had little impact on the final composition. Therefore, we focused on the analysis of Lasso and OMP results. The order of the factors filtered by Lasso and OMP differed more markedly. With the exception of factors Tguidance4 and Sbasic7, which do not change in ranking, all other factors increased (e.g., Tguidance7) or decreased (e.g., Tguidance3) (Figure 3c). In addition, Tbasic3 in OMP and Tbasic1 in Lasso are not reflected in the results selected by the other algorithm, respectively, which is the main difference between the two algorithms' filtering results. In order to combine the results of the two feature selection methods, the attribute set is added to the same degree, that is, in the Lasso and OMP, the attributes of A, B, C, and D grades are assigned 8, 6, 4, and 2 scores (0 points are not selected for filtering), and then the average value is obtained. For example, Tguidance3 has a score of 4 in Lasso, a score of 2 in OMP, and the final score of Tguidance3 is 3 on the average of 2 and 4. The weighted scores are then used to re-rank the importance of the factors. Thus, we obtain the following division [1, 2.75]-D, [2.75, 5.5]-C, [5.5, 6.25]-B and [6.25, 8]-A.  
Figure 3: A presentation of the ranking results in three factor selection methods, i.e., Lasso, EN, and OMP (**a**). When the factor that users are interested in is selected, the other factors’ colour will be lightened to highlight the selected one. (**b**) shows the comparison of changes in factors ranking (Tguidance5 and Sbasic4) between Lasso and EN, which presents a small change in ranking. While, (**c**) shows the large changes in factors ranking (Tguidance3 and Tguidance7) between Lasso and OMP. As can be seen from Figure 4, 11 relatively important factors were finally selected from the set of 19 factors, namely \"Supervisor's requirements for paper publication\", \"Participation of supervisor's projects\", \"Plan after graduation\", \"Participation of academic competition\", \"Recent graduates\", \"Frequency of academic discussion\", \"Supervisor's disadvantages\", \"Communication way\", \"Participation of academic training\", \"Number of students for each supervisor\" and \"Supervisor's guiding way\". As there is no B-level factor, the A-level factor \"Supervisor's requirements for paper publication\" plays a decisive role in whether the graduates can publish or not. For postgraduates intending to publish, they should select a supervisor who has a strong requirement for their papers. Four of the eight C-level factors are related to the students themselves, i.e., \"Plan after graduation\", \"Participation of academic competition\", \"Recent graduates\" and \"Participation of academic training\", which shows that the higher the level of participation in academic competitions, lectures and professional academic training, the higher the likelihood of scientific publication. The remaining four relate to supervisor guidance, which are \"Participation of supervisor's projects\", \"Frequency of academic discussion\", \"Supervisor's disadvantages\", and \"Communication way\", which reveal that the more frequently a graduate student is involved in supervisor's projects or academic discussions, the more likely he publish papers.