# Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks

@article{Tang2020TowardsSG, title={Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks}, author={Hao Tang and Zhiao Huang and Jia-Yuan Gu and Bao-Liang Lu and Hao Su}, journal={ArXiv}, year={2020}, volume={abs/2010.13547} }

Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems. Taking the perspective of synthesizing graph theory programs, we propose several extensions to address the issue. First, inspired by the dependency of the iteration number of common graph theory algorithms on graph size, we learn to terminate the message passing process in GNNs adaptively according to the computation…

## 8 Citations

### From Local Structures to Size Generalization in Graph Neural Networks

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This work revisits why the existing neural networks cannot maintain transformation invariance when handling geometric data and proposes Transformation Invariant Neural Networks (TinvNN), a straightforward and general framework for geometric data.

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