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Found 3 papers, 2 papers with code
Leveraging the Graph Structure of Neural Network Training Dynamics
Understanding the training dynamics of deep neural networks (DNNs) is important as it can lead to improved training efficiency and task performance.
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding
Network alignment, the process of finding correspondences between nodes in different graphs, has many scientific and industrial applications.
Heterogeneous network approach to predict individuals' mental health
To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source.
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