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Retrieved on: 2024-03-19 19:49:11
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Summary
The article discusses the impact of randomness in Graph Neural Network (GNN) performance, specifically for community detection tasks. It compares three metrics for assessing algorithm consistency under variability due to hyperparameter settings. The study highlights the importance of hyperparameter optimization in GNNs to avoid performance loss and proposes the robust W Randomness coefficient for evaluating randomness in GNNs. The tags and key concept are directly related to this research on optimizing and understanding the performance and variability in GNNs.
Article found on: medium.com
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