Xiangyu Zhao
Xiangyu Zhao
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Investigating GNN Expressiveness in Graph Generation Tasks
Imperial UROP 2023 Student Project – We propose an approach to improve GNN-based graph generative models including GCPN, GraphAF and GraphEBM, and investigate the correlation between GNN expressiveness in the graph prediction and graph generation contexts.
Xiandong Zou
,
Xiangyu Zhao
,
Pietro Liò
,
Yiren Zhao
21 Aug 2023
Investigating GNN Expressiveness in Graph Generation Tasks
Imperial UROP 2023 Student Project – We propose an approach to improve GNN-based graph generative models including GCPN, GraphAF and GraphEBM, and investigate the correlation between GNN expressiveness in the graph prediction and graph generation contexts.
Xiandong Zou
,
Xiangyu Zhao
,
Pietro Liò
,
Yiren Zhao
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
We propose an approach to improve GNN-based graph generative models including GCPN, GraphAF and GraphEBM, and investigate the correlation between GNN expressiveness in the graph prediction and graph generation contexts.
Xiandong Zou
,
Xiangyu Zhao
,
Pietro Liò
,
Yiren Zhao
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