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Showing 1 to 5 of 5 for “"graph coarsening"”.

  1. Efficient and Effective Algorithms for Controllable Fluid Simulation and Mesh Deformation

    … operators combined with a simple and effective graph coarsening strategy. The algorithm has very good performance and scalability.

    uiuc Repository record for Efficient and Effective Algorithms for Controllable Fluid Simulation and Mesh Deformation (opens in a new tab)

  2. Benchmarking Graph Transformers Toward Scalability for Large Graphs

    Graph transformers (GTs) have gained popularity as an alternative to graph neural networks (GNNs) for deep learning on graph-structured data. In particular, the self-attention mechanism of GTs mitigates the fundamental limitations of over-squashing, over-smoothing, and limited expressiveness that …

    mit Repository record for Benchmarking Graph Transformers Toward Scalability for Large Graphs (opens in a new tab)

  3. Topological Deep Learning: Graphs, Complexes, Sheaves

    The types of spaces where data resides - graphs, meshes, grids, manifolds - are becoming increasingly varied and heterogeneous. Therefore, translating ideas, models, and theoretical results between different domains is becoming more and more challenging. Nonetheless, two fundamental principles …

    cambridge Repository record for Topological Deep Learning: Graphs, Complexes, Sheaves (opens in a new tab)

  4. Optimizing and Understanding Network Structure for Diffusion

    … epidemic can die out quickly, if vulnerable demographic groups are successfully targeted for vaccination. Hence in this thesis, we aim to optimize and understand network structure better in light of diffusion. We optimize graph topologies by removing nodes/edges for controlling rumors/viruses …

    vt Repository record for Optimizing and Understanding Network Structure for Diffusion (opens in a new tab)