# MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
## 5 Application of the Model
### Inductive Learning Task  
In reality, new nodes are often input into the original network, which can be regarded as a dynamic network. Most dynamic network embedding methods obtain the representation of each node by modeling the dynamics of the network structure. However, the dynamics of network structure depend only on the appearance or disappearance of edges, regardless of the change of nodes. In order to get a representation of the new node, one way is to input the entire dataset containing the old and new nodes into the model for training, but the parts that have already been trained have to be retrained. To solve this problem, it is important to get the representations of the new input node from the existing nodes, which is part of this article. The MSGCN model has obtained the vector representation of all known nodes, on which the inductive learning task can be carried out. That is, the vector representations of the existing node are used to obtain the new node representations. The reason we can use the representations of known nodes to get the representations of new nodes is that a node tends to have a close relationship with its neighbors. In the case of citation networks, there is a high chance that scholars in one field of research will produce papers that remain in the same field. A large proportion of the papers cited in a paper are still in the same field. So, we can determine the label of the node according to its neighbors, and here we only consider the one-hop neighbors. For each new node \(v_{i}\in V_{new}\), a neighbor set of fixed size \(m\) is sampled to obtain a matrix \(A_{sample}\). The representation of each new node \(V_{i}\) is shown in Equation (9), and the representation of the new node is obtained through the aggregation of neighbor nodes' information. \[H_{isample}=(H[A_{isample}[1]+...+H[A_{isample}[m]])/m \tag{9}\]  
As shown in Figure 3, the new node representation can be obtained directly without the need to retrain the old node, which saves a lot of time. The sampling method here refers to the GraphSAGE model. For nodes with more than \(m\) neighbors, random sampling is performed. For nodes with fewer than \(m\) neighbors, repeat the sampling until \(m\) neighbors are collected. This allows each node to have the same number of neighbors and makes it easier to aggregate. However, this method also has some shortcomings. 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 proved that the accuracy of label classification in the inductive learning task is actually slightly lower than that directly trained by the MSGCN model. To improve the accuracy of the inductive learning task, we decide to start with the sampling neighbors. We consider that we can assign different weights to different neighbors. If we can get a set of important nodes, we can improve the accuracy of node classification with the help of this set. Of course, we cannot just use this node set. Because other neighbors can also affect the result, even if all neighbors of a node are not in the set. How to distinguish which nodes are important. Intuitively, important nodes should have large degrees. However, assigning large weights directly to these nodes does not seem to be persuasive. However, if a node is important to many other nodes, then we can consider it an important node. With the help of the attention mechanism, we can obtain the attention matrix \(W_{att}\), which reflects the importance between each node pair. For a center node \(v_{i}\) and a neighbor \(v_{j}\), self-attention is performed to compute attention coefficient \(e_{ij}\).