Domain Generalization Deep Graph Transformation

Shiyu Wang, Guangji Bai, Qingyang Zhu, Zhaohui Qin, Liang Zhao

Primary Area: general machine learning (i.e., none of the above)

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Keywords: graph transformation, domain generalization, hypernetworks

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TL;DR: MultiHyperGNN is a framework for graph transformation that achieves domain generalization via novel hypernetworks.

Abstract: Graph transformation that predicts graph transition from one mode to another is an important and common problem. Despite much recent progress in developing advanced graph transformation techniques, the fundamental assumption typically required in machine-learning models that the testing and training data preserve the same distribution does not always hold. As a result, domain generalization graph transformation that predicts graphs not available in the training data is under-explored, with multiple key challenges to be addressed including (1) the extreme space complexity when training on all input-output mode combinations, (2) difference of graph topologies between the input and the output modes, and (3) how to generalize the model to target domains that are not in the training data. To fill the gap, we propose a multi-input, multi-output, hypernetwork-based graph neural network (MultiHyperGNN) that employs a encoder and a decoder to encode both input and output modes and semi-supervised link prediction to enhance the graph transformation task. Instead of training on all mode combinations, MultiHyperGNN preserves a constant space and polynomial computational complexity with the encoder and the decoder produced by two novel hypernetworks. Comprehensive experiments show that MultiHyperGNN has a superior performance than competing models in both prediction and domain generalization tasks.

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Submission Number: 4353