GRAND+: Scalable Graph Random Neural Networks

TL;DR

GRAND+ introduces a scalable GNN framework with a novel algorithm and loss function, enabling efficient large-scale graph learning with improved accuracy and reduced computational costs.

Contribution

It presents GRAND+, a scalable GNN framework with GFPush algorithm and confidence-aware loss, addressing scalability and performance issues of prior GRAND models.

Findings

GRAND+ scales efficiently to large graphs.

It achieves better accuracy than existing scalable GNNs.

It reduces running time compared to other methods.

Abstract

Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate state-of-the-art performance for this problem. However, it is difficult for GRAND to handle large-scale graphs since its effectiveness relies on computationally expensive data augmentation procedures. In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning. To address the above issue, we develop a generalized forward push (GFPush) algorithm in GRAND+ to pre-compute a general propagation matrix and employ it to perform graph data augmentation in a mini-batch manner. We show that both the low time and space complexities of GFPush enable GRAND+ to efficiently scale to large graphs. Furthermore, we introduce a confidence-aware consistency loss into the model optimization of GRAND+, facilitating GRAND+'s generalization superiority. We conduct extensive experiments on seven public datasets of different sizes. The results demonstrate that GRAND+ 1) is able to scale to large graphs and costs less running time than existing scalable GNNs, and 2) can offer consistent accuracy improvements over both full-batch and scalable GNNs across all datasets.