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Found 4 papers, 1 papers with code
Graph topological property recovery with heat and wave dynamics-based features on graphs
In this paper, we propose Graph Differential Equation Network (GDeNet), an approach that harnesses the expressive power of solutions to PDEs on a graph to obtain continuous node- and graph-level representations for various downstream tasks.
Neural FIM for learning Fisher Information Metrics from point cloud data
Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature.
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
In GAE the latent space distance between points is regularized to match a novel multiscale geodesic distance on the data manifold that we define.
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data
We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature.
