GraphWeave learns graph family patterns via random walk trajectories and reconstructs new graphs through joint optimization, outperforming diffusion baselines on benchmarks for structures like communities and degree distributions while running 10x faster.
Chemical science9(2), 513–530 (2018)
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FlexMS is a new flexible benchmarking framework that lets researchers dynamically combine deep learning architectures and evaluate their mass spectrum prediction performance on public metabolomics datasets using multiple metrics and retrieval tasks.
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GraphWeave: Interpretable and Robust Graph Generation via Random Walk Trajectories
GraphWeave learns graph family patterns via random walk trajectories and reconstructs new graphs through joint optimization, outperforming diffusion baselines on benchmarks for structures like communities and degree distributions while running 10x faster.
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FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics
FlexMS is a new flexible benchmarking framework that lets researchers dynamically combine deep learning architectures and evaluate their mass spectrum prediction performance on public metabolomics datasets using multiple metrics and retrieval tasks.