Meta-structure-based graph attention networks.

Abstract

Due to the ubiquity of graph-structured data, Graph Neural Network (GNN) have been widely used in different tasks and domains and good results have been achieved in tasks such as node classification and link prediction. However, there are still many challenges in representation learning of heterogeneous networks. Existing graph neural network models are partly based on homogeneous graphs, which do not take into account the rich semantic information of nodes and edges due to their different types; And partly based on heterogeneous graphs, which require predefined meta-structures (include meta-paths and meta-graphs) and do not take into account the different effects of different meta-structures on node representation. In this paper, we propose the MS-GAN model, which consists of four parts: graph structure learner, graph structure expander, graph structure filter and graph structure parser. The graph structure learner automatically generates a graph structure consisting of useful meta-paths by selecting and combining the sub-adjacent matrices in the original graph using a 1 × 1 convolution. The graph structure expander further generates a graph structure containing meta-graphs by Hadamard product based on the previous step. The graph structure filterer filters out graph structures that are more effective for downstream classification tasks based on diversity. The graph structure parser assigns different weights to graph structures consisting of different meta-structures by a semantic hierarchical attention. Finally, through experiments on four datasets and meta-structure visualization analysis, it is shown that MS-GAN can automatically generate useful meta-structures and assign different weights to different meta-structures.