Adaptive propagation deep graph neural networks

Abstract

Graph neural networks (GNNs) with adaptive propagation combinations represent a specialized deep learning paradigm, engineered to capture complex nodal interconnections within graph data. The primary challenge of this model lies in distilling and representing features extracted over varying nodal distances. This paper delves into an array of adaptive propagation strategies, with a focus on the influence of nodal distances and information aggregation on model efficacy. Our investigation identifies a critical performance drop in scenarios featuring overly brief propagation paths or an insufficient number of layers. Addressing this, we propose an innovative adaptive propagation technique in deep graph neural networks, named AP-DGNN, aimed at reconstructing high-order graph convolutional neural networks (GCNs). The AP-DGNN model assigns unique aggregation combination weights to each node and category, culminating in a final model representation through a process of weighted aggregation. Notably, these weights are capable of assimilating both subjective and objective information characteristics within the network. To substantiate our model’s effectiveness and scalability, we employed often-used benchmark datasets for experimental validation. A notable aspect of our AP-DGNN model is its minimal training parameter requirement and reduced computational demand. Furthermore, we demonstrate the model’s enhanced performance, which remains consistent across various hyperparameter configurations. This aspect was rigorously tested under diverse hyperparameter settings. Our findings contribute significantly to the evolution of graph neural networks, potentially revolutionizing their application across multiple domains. The research presented herein not only advances the understanding of GNNs but also paves the way for their robust application in varied scenarios. Codes are available at https://github.com/CW112/AP_DGNN.