# MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
## 6 Experiment
### Inductive Learning Task  
After analyzing three datasets, we find the DBLP is too dense, in which the neighbors of each node have more than 500 neighbors on average. So we verify the performance of models on ACM and AMiner datasets. The experimental results are shown in Figure 5. It can be seen that the accuracy is still over 80%, slightly lower than the result obtained by direct training with the MSGCN model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**MethodDataset** & **DBLP** & **AMiner** & **ACM** \\ \hline DeepWalk & 0.8736 & 0.904 & 0.8386 \\ GF & 0.8061 & 0.7867 & 0.724 \\ HOPE & 0.9064 & 0.86 & 0.8213 \\ SDNE & 0.8983 & 0.804 & 0.8227 \\ \hline GAT & 0.5683 & 0.9066 & 0.6777 \\ GCN & 0.6566 & 0.9036 & 0.6767 \\ TAGCN & 0.7851 & 0.9147 & 0.8353 \\ \hline HAN & 0.8484 & 0.9006 & 0.8444 \\ MuSDAC & 0.8170 & 0.7990 & 0.7900 \\ GraphInception & 0.7090 & 0.6030 & 0.5930 \\ \hline MSGCN & **0.9096** & **0.9277** & **0.8594** \\ MSGCN-mean & **0.8203** & **0.9327** & **0.8695** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results.  
At the beginning of the induction learning task, the neighbors of the nodes sampled are all obtained through the MSGCN model, and the representation of new nodes also has a good effect. However, in this task, we do not use the feature information of nodes, but only obtain the representation vector by aggregating the information of neighbors. Without the help of feature information, the effect in the downstream task will also be deficient. Additionally, the experiment also proves that the accuracy of label classification in the inductive learning task is actually slightly lower than that directly trained by the MSGCN model.  
In spite of this, in fact, the accuracy is still higher than that of most models. However, as time goes by, the latest nodes in the future will be neighbors of those nodes with lower accuracy, so the accuracy will further decline. However, the number of new nodes in our experiment is the same as the number of trained nodes in MSGCN, so the effect of the inductive task can still be guaranteed in the short term. However, in order to maintain accuracy, when enough new nodes appear, it is better to use the MSGCN model to train all  
Figure 4: The visualization results of the models. Models include HOPE, TAGCN, HAN, and MSGCN. We conduct experiment on node classification task on three dataset. The visualization results demonstrate the superiority of our model.  
Figure 5: The experimental result of the inductive learning task. The accuracy is little lower than that of the MSGCN model.  
the data. Because the feature information is used in MSGCN, retraining ensures that the inductive task remains accurate for a long time.  
After that, we fine-tune the experimental settings only to explore the influence of assigning different weights to important nodes on the accuracy in the node classification task of new nodes. We consider the nodes that occur more than 10 times in the sampling matrix \(M\) as important nodes. The default initial weight of all nodes is 1. The experimental results are shown in Figure 6. It can be seen that assigning an appropriately high weight to important nodes will indeed improve the accuracy of node classification, but the weight should not be too high. Numerous experiments have shown that 1.5 is an appropriate weight.