## 5 Experiment and Discussion
### Experiment Result and Discussion
#### 5.3.6 Each Sub Module and Its Parameter Influence  
In order to verify the effectiveness of each module and the influence of different parameters, we have conducted the following comparison experiments. The specific definitions of experiments are as follows:  
1. MRDE, representing the method in this paper;
2. MRDE-NoF, indicating that node features are not used;
3. MDE-Bili, which means that the meta path instance is encoded with Bilinear [34];  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Dataset**} \\ \cline{2-3}  & **DBLP** & **IMDB** \\ \hline Raw & 1032.19 & 863.56 \\ GCN & 4563.25 & 2232.41 \\ GAT & 7563.43 & 4509.28 \\ M2V & 1589.68 & 1097.65 \\ JUST & 7192.086 & 30,122.36 \\ CTDNE & 32,858.93 & 14,118.73 \\ C2V & **256.37** & **187.15** \\ MRDE & **815.06** & **327.92** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Clustering task running time (in seconds).  
Figure 4: The effect of temporal variation on the classification task on the DBLP dataset.  
* MDE-Tran, which means that the meta path instance is encoded with TransE [33];
* MRDE-Avg, the coding method of averaging node vectors;
* MRDE-NoT means that the time attribute in the network is not considered, and only the attention mechanism is used to fuse the meta path instances;
* MRDE-NoAtt, which means that only the time attribute in the network is considered, and no attention mechanism is used to fuse meta-path instances;
* MRDE-NoC, which means that the central node of the meta path instance is not considered, and only the node pairs connected by the meta path are used.  
The index values of each method in node classification tasks are shown in Figure 5, and the index values of node clustering tasks are shown in Figure 6.  
It can be seen from Figures 5 and 6 that:  
1. The MRDE-NoF method is the worst or second worst for both classification tasks and clustering tasks, indicating that the attribute information of the node itself has a great impact on the performance of embedding. By observing (a) and (b), the degree of influence in the DBLP dataset is obviously lighter than that in the IMDB dataset. We  
Figure 5: Node classification task. (a) DBLP. (b) IMDB.  
Figure 6: The ARI values of the node clustering task. (a) DBLP. (b) IMDB.  
judge that the core information in the DBLP dataset is structural information and time information, while the core information in the IMDB dataset contains both structural information, time information, and attribute information;
2. The performance of the MDE-Bili and MDE-Tran methods on DBLP and IMDB datasets is quite different, which indicates that translation models and bilinear models are more dependent on the matching degree between the data's internal patterns and models. When the matching degree is high, good results can be achieved. Otherwise, the effects may decline significantly;
3. MRDE-NoT has different effects on different classification tasks and different data sets, but it is weaker than MRDE, indicating that the impact of time attribute information is unstable, but it will have a certain impact on the final embedding effect. MRDE-NoAtt, MRDE-Avg, and MRDE-NoC methods have similar characteristics;
4. The MRDE model has achieved the best results in all data sets and tasks, indicating the rationality of the embedding framework we have adopted.