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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, theinherently inter-connected nature precludes parallelization within the graph,which becomes critical for large-sized graph, as memory constraints limitbatching across the nodes. In this paper, we will introduce a new graph neuralnetwork, namely GRAPH-BERT (Graph based BERT), solely based on the attentionmechanism without any graph convolution or aggregation operators. Instead offeeding GRAPH-BERT with the complete large input graph, we propose to trainGRAPH-BERT with sampled linkless subgraphs within their local contexts.GRAPH-BERT can be learned effectively in a standalone mode. Meanwhile, apre-trained GRAPH-BERT can also be transferred to other application tasksdirectly or with necessary fine-tuning if any supervised label information orcertain application oriented objective is available. We have tested theeffectiveness of GRAPH-BERT on several graph benchmark datasets. Based thepre-trained GRAPH-BERT with the node attribute reconstruction and structurerecovery tasks, we further fine-tune GRAPH-BERT on node classification andgraph clustering tasks specifically. The experimental results have demonstratedthat GRAPH-BERT can out-perform the existing GNNs in both the learningeffectiveness and efficiency.

Graph-Bert: Only Attention is Needed for Learning Graph Representations