GraphHI: Boosting Graph Neural Networks for Large-Scale Graphs

To analyze and process graph data, researchers have proposed Graph Neural Network (GNN) models. In this paper, we focus on methods for boosting the performance of existing GNN models and propose GraphHI, a GNN framework that integrates Hidden Insights to enhance the performance of a given GNN model. We propose to utilize both inter-model and intra-model hidden insights. The inter-model hidden insights encompass the embedding vectors and logit vectors derived from other pretrained models using the same graph data. The intra-model hidden insights incorporate the embedding vectors of other nodes from the same GNN model. To optimize the suitability of hidden insights for GNN model training, we conduct a theoretical analysis of the influence of various forms of the transformed logits and the parameter
$T$
in the data transformation function. Based on this analysis, a method for setting dynamic personalized parameters in the data transformation is proposed, which is tailored to the current state of each node in the GNN model. To integrate multiple sources of hidden insights, we propose ALC, an algorithm that dynamically sets appropriate combination coefficients for various loss terms. The experimental results show that GraphHI can boost the performance of GNN models using different pretrained models in four different tasks.