# MBHAN: Motif-Based Heterogeneous Graph Attention Network
## 1 Introduction  
Graph neural networks (GNNs) have attracted extensive attention in academia as a powerful way of approaching deep representation learning for graph data. They have been proven to perform especially well in network analysis [1, 2]. The basic idea of a graph neural network is to undertake representation learning on the nodes themselves, according to their local neighborhood information. This involves aggregating the information of each node and its surrounding nodes through a neural network. So, in [3, 4, 5], the node features and graph structure in graphs are used to learn node embeddings. Convolutional operations can also be introduced into graph representation learning [6, 7, 8, 9]. Alongside GNNs, a significant amount of interest has been shown to attention mechanisms [10], which encourage models to focus on the most salient parts of the data that will affect downstream tasks. Attention mechanisms have been highly effective when incorporated into deep neural network frameworks and are widely used across a range of different domains [11, 12, 13, 14, 15]. Graph Attention Networks (GAT) [16] assume that different neighboring nodes may play different roles for the core nodes. A self-attention mechanism can therefore be employed [17] to aggregate neighbor nodes and achieve an adaptive matching of weights that captures the different importance of different neighbors. However, GAT can only be applied to homogeneous graphs and cannot be easily migrated to heterogeneous graphs. Heterogeneous graph-based neural network representation learning methods are a natural extension of deep learning approaches to the processing of structured graph data. The most popular approach, here, is to transform heterogeneous graphs into homogeneous graphs for representation learning through meta-paths [18]. [PERSON] et al. [19], for instance, introduced a two-level hierarchical attention mechanism in graph neural networks where the node-level attention captures the relationship between neighboring nodes generated by a certain meta-path, while meta-path semantic attention captures the importance of the different meta-paths in the original heterogeneous graph. To make the most of the rich interaction information present in heterogeneous graphs focused on intent recommendation, [PERSON] et al. [20] used meta-path guided neighbors to aggregate the node information and designed different aggregation functions based on different types of neighbor features. All of these methods require an expert to design and manually set the meta-paths for specific issues. There is also inevitably a loss of information during the transformation between heterogeneous and homogeneous graphs, and the selection of different meta-paths can lead to significant performance fluctuations in downstream tasks [21]. Numerous studies [22; 23; 24; 25; 26; 27; 28; 29; 30] have verified the superior performance of motifs as fundamental building blocks of non-random real-world graph-structured data for the purposes of graph representation learning. A motif is essentially a subgraph structure consisting of multiple nodes and links. The semantic relationships among the set of nodes that make up a motif are particularly closely related. Thus, it seems reasonable to assume that, if the representation of nodes in motif-based subgraphs with different structural patterns can be captured, it will be possible to characterize different types of nodes in heterogeneous graphs from multiple perspectives. The Multiscale Convolutional Network (MCN) [30] constructed graph convolutional neural network introduces multiple weighted motif-based adjacency matrices to capture higher-order neighborhood information. [PERSON] et al. [31] used motifs for subgraph normalization and designed the Motif-based Attentional Graph Convolutional Neural Network (MA-GCNN) for subgraph classification tasks. However, the MCN, here, is still constrained to the pre-design of the motif structure by an expert and does not consider the motif schemans of other morphologies in the graph.