  The dominant graph neural networks (GNNs) over-rely on the graph links,
several serious performance problems with which have been witnessed already,
e.g., suspended animation problem and over-smoothing problem. What's more, the
inherently inter-connected nature precludes parallelization within the graph,
which becomes critical for large-sized graph, as memory constraints limit
batching across the nodes. In this paper, we will introduce a new graph neural
network, namely GRAPH-BERT (Graph based BERT), solely based on the attention
mechanism without any graph convolution or aggregation operators. Instead of
feeding GRAPH-BERT with the complete large input graph, we propose to train
GRAPH-BERT with sampled linkless subgraphs within their local contexts.
GRAPH-BERT can be learned effectively in a standalone mode. Meanwhile, a
pre-trained GRAPH-BERT can also be transferred to other application tasks
directly or with necessary fine-tuning if any supervised label information or
certain application oriented objective is available. We have tested the
effectiveness of GRAPH-BERT on several graph benchmark datasets. Based the
pre-trained GRAPH-BERT with the node attribute reconstruction and structure
recovery tasks, we further fine-tune GRAPH-BERT on node classification and
graph clustering tasks specifically. The experimental results have demonstrated
that GRAPH-BERT can out-perform the existing GNNs in both the learning
effectiveness and efficiency.
