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Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
Prior attacks on graph neural networks have mostly focused on graph poisoning and evasion, neglecting the network's weights and biases.
On the Two Sides of Redundancy in Graph Neural Networks
Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes.
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