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

G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning