@inproceedings{feng2022grand,
author = {Feng, Wenzheng and Dong, Yuxiao and Tinglin, Huang and Yin, Ziqi and Cheng, Xu and Kharlamov, Evgeny and Tang, Jie},
title = {GRAND+: Scalable Graph-based Semi-Supervised Learning with Better Generalization},
booktitle = {TheWebConf 2022},
year = {2022},
month = {April},
abstract = {Graph neural networks (GNNs) have been widely adopted for semisupervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate state-ofthe-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.},
url = {http://approjects.co.za/?big=en-us/research/publication/grand-scalable-graph-random-neural-networks/},
}