48 citations · 213 across the 21 of their papers we have counts for
4 papers · 1 filter
Higher-Order Positional Encodings for Graph Representation Learning
Caleb Stam, Aagrim Hoysal, Sanjukta Krishnagopal
Many real-world systems exhibit higher-order interactions among groups of entities that cannot be captured by pairwise relationships alone. Graph Transformers and Graph Neural Netw…
Sparse Contextual Coupling Reshapes Diffusion Geometry in Multilayer Hypergraphs
Hao Ding, Sanjukta Krishnagopal
Many complex systems combine dense background structure with sparse contextual information. We introduce a diffusion-based framework for analyzing how sparse condition-specific lay…
Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
Sanjukta Krishnagopal
Spectral graph sparsification is a classical tool for reducing graph complexity while preserving Laplacian quadratic forms. In graph neural networks (GNNs), sparsification is often…
Topological Neural Tangent Kernel
Sanjukta Krishnagopal
Graph neural tangent kernels give a principled infinite-width theory for graph neural networks, but inherit a basic limitation of graph models: they see only pairwise structure. Ma…