Hierarchy-Aware Adaptive Graph Neural Network

Graph Neural Networks (GNNs) have gained attention for their ability in capturing node interactions to generate node representations. However, their performances are frequently restricted in real-world directed networks with natural hierarchical structures. Most current GNNs incorporate information from immediate neighbors or within predefined receptive fields, potentially overlooking long-range dependencies inherent in hierarchical structures. They also tend to neglect node adaptability, which varies based on their positions. To address these limitations, we propose a new model called Hierarchy-Aware Adaptive Graph Neural Network (HAGNN) to adaptively capture hierarchical long-range dependencies. Technically, HAGNN creates a hierarchical structure based on directional pair-wise node interactions, revealing underlying hierarchical relationships among nodes. The inferred hierarchy helps to identify certain key nodes, named Source Hubs in our research, which serve as hierarchical contexts for individual nodes. Shortcuts adaptively connect these Source Hubs with distant nodes, enabling efficient message passing for informative long-range interactions. Through comprehensive experiments across multiple datasets, our proposed model outperforms several baseline methods, thus establishing a new state-of-the-art in performance. Further analysis demonstrates the effectiveness of our approach in capturing relevant adaptive hierarchical contexts, leading to improved and explainable node representation.