Effective and Scalable Heterogeneous Graph Neural Network Framework with Convolution-oriented Attention

The heterogeneous graph, as an effective representation of real-world data, encapsulates rich structural and semantic information. In recent years, numerous Heterogeneous Graph Neural Networks (HGNNs) have been proposed to learn node representations on heterogeneous graphs. Although existing methods have introduced various unique information aggregation and semantic fusion mechanisms, they still exhibit limitations in effectiveness and scalability. In this study, we introduce the gatekeeping theory in heterogeneous graph learning and investigate the primary challenges limiting current HGNNs. To address these challenges, we propose a novel, effective, and scalable heterogeneous graph neural network framework, the Heterogeneous Convolution-oriented Attention Network (HCAN). HCAN enhances the heterogeneous attention mechanism to learn far-sighted weights by encoding long-range relation information into node representation with a convolutional subgraph encoder. To further improve heterogeneous graph representation learning, we propose effective and scalable models based on the HCAN framework. We evaluate HCAN on various commonly used heterogeneous datasets and show that it outperforms the state-of-the-art methods, especially on challenging datasets.