  The recent GRAPH-BERT model introduces a new approach to learning graph
representations merely based on the attention mechanism. GRAPH-BERT provides an
opportunity for transferring pre-trained models and learned graph
representations across different tasks within the same graph dataset. In this
paper, we will further investigate the graph-to-graph transfer of a universal
GRAPH-BERT for graph representation learning across different graph datasets,
and our proposed model is also referred to as the G5 for simplicity. Many
challenges exist in learning G5 to adapt the distinct input and output
configurations for each graph data source, as well as the information
distributions differences. G5 introduces a pluggable model architecture: (a)
each data source will be pre-processed with a unique input representation
learning component; (b) each output application task will also have a specific
functional component; and (c) all such diverse input and output components will
all be conjuncted with a universal GRAPH-BERT core component via an input size
unification layer and an output representation fusion layer, respectively.
  The G5 model removes the last obstacle for cross-graph representation
learning and transfer. For the graph sources with very sparse training data,
the G5 model pre-trained on other graphs can still be utilized for
representation learning with necessary fine-tuning. What's more, the
architecture of G5 also allows us to learn a supervised functional classifier
for data sources without any training data at all. Such a problem is also named
as the Apocalypse Learning task in this paper. Two different label reasoning
strategies, i.e., Cross-Source Classification Consistency Maximization (CCCM)
and Cross-Source Dynamic Routing (CDR), are introduced in this paper to address
the problem.
