GRAND+: Scalable Graph Random Neural Networks

12 Mar 2022  ·  Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Node Classification MAG-scholar-C FastGCN Accuracy 64.3 # 1
Node Classification MAG-scholar-C GraphSAINT Accuracy 75.0 # 3
Node Classification MAG-scholar-C GRAND+ Accuracy 80.0 # 4
Node Classification MAG-scholar-C PPRGo Accuracy 72.9 # 2


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