For linearized graph convolutional networks, the paper derives an exact expression for classification loss under partial noisy observations and uses it to design a greedy sampling scheme that can beat both random and reconstruction-optimal sampling.
Low-complexity graph sampling with noise and signal reconstruction via neumann series,
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On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals
For linearized graph convolutional networks, the paper derives an exact expression for classification loss under partial noisy observations and uses it to design a greedy sampling scheme that can beat both random and reconstruction-optimal sampling.