  We present Adaptive Multi-layer Contrastive Graph Neural Networks (AMC-GNN),
a self-supervised learning framework for Graph Neural Network, which learns
feature representations of sample data without data labels. AMC-GNN generates
two graph views by data augmentation and compares different layers' output
embeddings of Graph Neural Network encoders to obtain feature representations,
which could be used for downstream tasks. AMC-GNN could learn the importance
weights of embeddings in different layers adaptively through the attention
mechanism, and an auxiliary encoder is introduced to train graph contrastive
encoders better. The accuracy is improved by maximizing the representation's
consistency of positive pairs in the early layers and the final embedding
space. Our experiments show that the results can be consistently improved by
using the AMC-GNN framework, across four established graph benchmarks: Cora,
Citeseer, Pubmed, DBLP citation network datasets, as well as four newly
proposed datasets: Co-author-CS, Co-author-Physics, Amazon-Computers,
Amazon-Photo.
