  In graph instance representation learning, both the diverse graph instance
sizes and the graph node orderless property have been the major obstacles that
render existing representation learning models fail to work. In this paper, we
will examine the effectiveness of GRAPH-BERT on graph instance representation
learning, which was designed for node representation learning tasks originally.
To adapt GRAPH-BERT to the new problem settings, we re-design it with a
segmented architecture instead, which is also named as SEG-BERT (Segmented
GRAPH-BERT) for reference simplicity in this paper. SEG-BERT involves no
node-order-variant inputs or functional components anymore, and it can handle
the graph node orderless property naturally. What's more, SEG-BERT has a
segmented architecture and introduces three different strategies to unify the
graph instance sizes, i.e., full-input, padding/pruning and segment shifting,
respectively. SEG-BERT is pre-trainable in an unsupervised manner, which can be
further transferred to new tasks directly or with necessary fine-tuning. We
have tested the effectiveness of SEG-BERT with experiments on seven graph
instance benchmark datasets, and SEG-BERT can out-perform the comparison
methods on six out of them with significant performance advantages.
