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Found 2 papers, 2 papers with code
Understanding microbiome dynamics via interpretable graph representation learning
Through our experiments, we show that we can extract graph features such as clusters of nodes or edges that have the highest impact on the model to learn the low-dimensional representation.
GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
We demonstrate that our method can capture temporary changes in the time-evolving graph on both created synthetic data and real-world data.
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