Node classification oriented Adaptive Multichannel Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) play an important role in accomplishing node classification on heterogeneous graphs (HGs). These models are built on the traditional spatial graph neural networks (GNNs) framework of neighborhood sampling, message passing, and aggregation. However, like GNNs, HGNNs face challenges in capturing high -order neighbor information without oversmoothing or classifying vertices with flexible topologies. To address these issues, we propose a novel Adaptive Multichannel Heterogeneous Graph Neural Network ( AMHGNN ) that adaptively utilizes H igh and L ow -frequency signals , resulting in a more accurate node classification with flexible topologies. Specifically, we first identify the vertices with the closest topological associations for each vertex using a Markov process, regardless of whether they are originally connected, and add them to the original HG as a new type of edge that directly captures the information from relevant arbitrary -order neighbors without oversmoothing. Second, we designed frequencyadaptive heterogeneous graph neural networks that map vertices and edges of different types to the same vector space, separate high- and low -frequency signals, and adaptively aggregate them for each vertex. Extensive experiments on four real -world HGs demonstrated that AMHGNN achieved excellent performance across various types of datasets, particularly those that demand more high -frequency signals. The code is available at https://github.com/LIyvqi/AMHGNN.