## 5 Experiment and Discussion
### Baselines  
In order to fully verify the effectiveness of our method, we use seven representation learning methods as baselines, including:  
1. Raw Feature, that is, the initial input features are used for node representation;
2. GCN [13], a homogeneous neural network model, introduces the neural network into the field of graph learning. Here we adapt standard experimental strategies and parameter settings and construct a meta-path-based adjacency matrix to facilitate its operation according to the requirements of homogeneous graphs;
3. GAT [14], a homogeneous neural network model, uses an attention mechanism. Here we adapt standard experimental strategies and parameter settings and construct a meta-path-based adjacency matrix to facilitate its operation according to the requirements of homogeneous graphs;
4. Metapath2 vec (M2V) [8], a representation learning method for HINs, performs random one-walk based on meta-paths. Here we set the window size to 5, the walk length to 100, and the number of negative samples to 5;
5. JUST [49], a static HIN embedding method. Here, the initial stay probability parameter of the walk is set to \(\alpha=0.2\) so that the algorithm tends to explore new types of nodes through heterogeneous edges during the walk, and the remaining parameters are selected as the default settings in the original text;
6. CTDNE [10] is a real-time dynamic homogeneous network embedding method in which random walks are organized with edges as angles, and the next edge is selected according to the strict increasing order of timestamps. Here the minimum length of each walk sequence is 5. The size of the batch update is consistent with the sliding window step size to ensure that the required walking sequence can be generated in the network with a limited number of timestamps and that the sequence is not too short.
7. Change2 vec (C2V) [50], a DHIN embedding method for snapshots, uses meta-path guidance. In order to ensure the quality of meta-path selection, the meta-paths used in each dataset are selected based on the semantics of the dataset. And considering the influence of selecting different meta-paths, multiple meta-paths are comprehensively considered for random walks in the experiment;  
In this work, the Adam optimizer [51] is used, and the learning rate is set to 0.01, the weight decay is 0.001, the node dimension is 512, the attention dimension is 8, and the experimental platform is Pytorch. In the node classification task, the learning method adopts the Support Vector Machine, while the supervised method is an end-to-end model, so the classification results are directly output. 80% of the data set is used as the training set, 10% of the data is used as the validation set, and 10% of the data is used as the test set. In order to ensure the stability of the results, all experimental tasks were repeated 10 times.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Node** & \begin{tabular}{c} **Node** \\ **Number** \\ \end{tabular} & **Edge** & \
\begin{tabular}{c} **Edge** \\ **Number** \\ \end{tabular} & **
\begin{tabular}{c} **Feature** \\ **Dimension** \\ \end{tabular} & **Meta-Path** \\ \hline \multirow{3}{*}{DBLP} & Author (A) & 4057 & \multirow{3}{*}{AP} & 19,645 & \multirow{3}{*}{334} & APA \\  & Paper (P) & 14,328 & & AC & 14,328 & \\  & Conference (C) & 20 & & PT & 88,420 & \\  & Term (T) & 8789 & & & \\ \hline \multirow{3}{*}{IMDB} & Movie (M) & 4275 & \multirow{3}{*}{MA} & 12,838 & \multirow{3}{*}{6344} & MAM \\  & Actor (A) & 5431 & & & 4280 & \\ \cline{1-1}  & Director (D) & 2082 & & MK & 20,529 & \\ \cline{1-1}  & Keyword (K) & 7313 & & & & MK \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset summary information.