Existing heterogeneous graph neural network algorithms (HGNNs) mostly rely on
meta-paths to capture the rich semantic information contained in heterogeneous
graphs (also known as heterogeneous information networks (HINs)), but most of
these HGNNs focus on different ways of feature aggre gation and ignore the
properties of the meta-paths themselves. This paper studies meta-paths in three
commonly used data sets and finds that there are huge differences in the number
of neighbors connected by different meta paths. At the same time, the noise
information contained in large neigh bor paths will have an adverse impact on
model performance. Therefore, this paper proposes a Heterogeneous Graph Neural
Network Classification and Aggregation Algorithm Based on Large and Small
Neighbor Path Iden tification(LSPI). LSPI firstly divides the meta-paths into
large and small neighbor paths through the path discriminator , and in order to
reduce the noise interference problem in large neighbor paths, LSPI selects
neighbor nodes with higher similarity from both topology and feature
perspectives, and passes small neighbor paths and filtered large neighbor paths
through different graph convolution components. Aggregation is performed to
obtain feature information under different subgraphs, and then LSPI uses
subgraph level attention to fuse the feature information under different
subgraphs to generate the final node embedding. Finally this paper verifies the
superiority of the method through extensive experiments and also gives
suggestions on the number of nodes to be retained in large neighbor paths
through exper iments. The complete reproducible code adn data has been
published at: this https URL

通过研究不同数据集中的元路径，本文发现不同元路径之间连接的邻居数量存在巨大差异，并针对大邻居路径中包含的噪声干扰问题，提出了基于大和小邻居路径识别的异构图神经网络分类和聚合算法（LSPI），通过路径判别器将元路径划分为大和小邻居路径，并从拓扑和特征角度选择具有较高相似性的邻居节点，通过不同的图卷积组件对小邻居路径和经过筛选的大邻居路径进行聚合，根据不同子图融合特征信息，生成最终节点嵌入。该方法在广泛的实验中验证了其优越性，并给出了关于保留大邻居路径中节点数量的建议。