关键词: 图卷积网络, 异配图, 图表示学习, Graph Transformer, 节点分类

Abstract:

Existing Graph Convolutional Network （GCN） methods are based on the assumption of homophily， which cannot be directly applied to heterophilic graph representation learning， and many studies on heterophilic graph representation learning are limited by message-passing mechanism， which leads to the problem of over-smoothing due to the confusion and over-squeezing of node features. To address these issues， a semi-supervised heterophilic graph representation learning model based on Graph Transformer，named HPGT（HeteroPhilic Graph Transformer）， was proposed. Firstly， the path neighborhood of a node was sampled using the degree connection probability matrix， then the heterophilic connection patterns of nodes on the path were adaptively aggregated through the self-attention mechanism， which were encoded to obtain the structural information of nodes， and the original attribute information and structural information of nodes were used to construct the self-attention module of the Transformer layer. Secondly， the hidden layer representation of each node itself was separated from those of its neighboring nodes and updated to avoid the node aggregating too much information about itself through the self-attention module， and then the representation and the neighborhood representation of nodes were connected to get the output of a single Transformer layer； in addition， the outputs of all Transformer layers were connected to get the final node hidden layer representation so as to prevent the loss of information in middle layers. Finally， the linear layer and Softmax layer were used to map the hidden layer representations of nodes to the predictive labels of nodes. In the comparison experiments with the model without Structural Encoding （SE）， SE based on degree connection probability provides effective deviation information for self-attention modules of Transformer layers， and improves the average accuracy of HPGT by 0.99% to 11.98%. Compared with the comparative models， on the heterophilic datasets （Texas， Cornell， Wisconsin， and Actor）， the node classification accuracies of HPGT are improved by 0.21% to 1.69%， and on homophilic datasets （Cora， CiteSeer， and PubMed）， the node classification accuracies reach 0.837 9， 0.746 7 and 0.886 2， respectively. The experimental results show that HPGT has a strong ability for heterogeneous graph representation learning， and is particularly suitable for node classification tasks of strong heterophilic graphs.

Key words: Graph Convolutional Network (GCN), heterophilic graph, graph representation learning, Graph Transformer, node classification