Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training

It is hard to directly implement Graph Neural Networks (GNNs) on large scaled graphs. Besides of existed neighbor sampling techniques, scalable methods decoupling graph convolutions and other learnable transformations into preprocessing and post classifier allow normal minibatch training. By replacing redundant concatenation operation with attention mechanism in SIGN, we propose Scalable and Adaptive Graph Neural Networks (SAGN). SAGN can adaptively gather neighborhood information among different hops. To further improve scalable models on semi-supervised learning tasks, we propose Self-Label-Enhance (SLE) framework combining self-training approach and label propagation in depth. We add base model with a scalable node label module. Then we iteratively train models and enhance train set in several stages. To generate input of node label module, we directly apply label propagation based on one-hot encoded label vectors without inner random masking. We find out that empirically the label leakage has been effectively alleviated after graph convolutions. The hard pseudo labels in enhanced train set participate in label propagation with true labels. Experiments on both inductive and transductive datasets demonstrate that, compared with other sampling-based and sampling-free methods, SAGN achieves better or comparable results and SLE can further improve performance.

Adaptive Graph Diffusion Networks with Hop-wise Attention

Graph Neural Networks (GNNs) have received much attention recent years and have achieved state-of-the-art performances in many fields. The deeper GNNs can theoretically capture deeper neighborhood information. However, they often suffer from problems of over-fitting and over-smoothing. In order to incorporate deeper information while preserving considerable complexity and generalization ability, we propose Adaptive Graph Diffusion Networks with Hop-wise Attention (AGDNs-HA). We stack multi-hop neighborhood aggregations of different orders into single layer. Then we integrate them with the help of hop-wise attention, which is learnable and adaptive for each node. Experimental results on the standard dataset with semi-supervised node classification task show that our proposed methods achieve significant improvements.