  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: https://github.com/liuhua811/LSPIA.
