# MBHAN: Motif-Based Heterogeneous Graph Attention Network
## 5 Experiments
### Multi-Class Classification  
Multi-class nodes are nodes' labels with more than two classes, but where each node is assigned to only one label in the graph. The multi-class classification prediction assigns one and only one label to each node. MBHAN uses a fully connected linear layer to perform the multi-class node classification task. We employed a 10-fold cross-validation experiment and report the average Macro-F1 and Micro-F1 performance for MBHAN and all the other baseline methods in Table 3.  
It can be seen that MBHAN outperformed the various benchmark methods. Out of the traditional graph representation learning methods, metapath2 vec with guided meta-paths guided performed better than DeepWalk. This again confirms that considering the heterogeneity of the nodes preserves more knowledge in the graph. The graph neural network-based methods, e.g., GAT and GCN, not only retained the structural information for the graph, but also attempted to fuse the node features. These methods performed better than the traditional graph representation learning methods. Looking more closely at the results, because of its differential treatment of the nodes' neighboring objects, GAT was better able to capture the degree of importance of the node neighbors in the graph than the simple GCN approach, where the neighboring nodes are merely averaged. When compared with GAT, HAN not only focused on relevant knowledge of the nodes and their neighbors, but also differentiated the influence of substructures (subgraphs that satisfied the meta-paths) on the final embedding of the nodes from a high-dimensional (meta-path) perspective. Unfortunately, it remains the case that HAN transforms heterogeneous graphs into homogeneous ones, so there was inevitably some loss of information and fluctuation in the effectiveness of the artificially selected meta-paths for the downstream tasks. Thus, GTN's convolution of multiple meta-path graphs enabled it to learn the importance of the length of the meta-paths more adaptively and gain a better performance than HAN. Unlike GTN, MBHAN not only has the advantages of the HAN hierarchical attention mechanism, but also avoids the need for homogeneous transformation of heterogeneous graphs by using motif subgraphs, so it does not require a priori meta-path knowledge. MBHAN can also classify different types of nodes, such as the Author node and Paper node in the DBLP_four_area dataset, simultaneously without needing to change the graph neural network model. So, to sum up, MBHAN performed better than GTN because it can handle non-Euclidean spatially heterogeneous graph data with high degrees of variance and because using a hierarchical attention mechanism enables it to better capture the differentiation between nodes than other graph neural network methods. As a final observation, note that the performance of MBHAN was also better than the simplified MBHAN\({}_{\text{non\_type}}\). This indicates that having different types of nodes in downstream tasks can mean that \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & \multicolumn{3}{c}{**DBLP_Four_Area**} & \multicolumn{3}{c}{**ACM**} \\ \hline
**Target Nodes** & \multicolumn{3}{c}{**Author**} & \multicolumn{3}{c}{**Paper**} & \multicolumn{3}{c}{**Paper**} \\ \hline
**Metrics** & **Mac-F1** & **Mic-F1** & **Mac-F1** & **Mic-F1** & **Mac-F1** & **Mic-F1** \\ \hline DeepWalk & 85.01 & 85.71 & 84.35 & 84.17 & 84.71 & 85.07 \\ metapath2 vec & 87.74 & 87.58 & 84.61 & 84.89 & 84.81 & 84.57 \\ GCN & 86.47 & 87.09 & 85.84 & 86.58 & 85.77 & 85.29 \\ GAT & 88.57 & 89.11 & 87.85 & 89.64 & 87.44 & 87.80 \\ HAN & 93.08 & 93.99 & 89.48 & 92.40 & 90.40 & 90.72 \\ GTN & 94.14 & 94.17 & 88.80 & 91.64 & 90.77 & 90.68 \\ \hline MBHAN\({}_{\text{non\_type}}\) & 94.47 & 94.14 & **90.00** & 93.43 & 90.71 & 90.32 \\ MBHAN & **95.45** & **95.57** & **90.00** & **93.65** & **91.17** & **90.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Node classification performance results (%).