4 papers
CAST: Canonical Approximate Schur Tree for Approximate Cholesky on Graphs
Meher Chaitanya, Cameron Musco, Aristides Gionis
Graph-data workloads such as diffusion estimation, ranking, semi-supervised learning, and network optimization often solve many Laplacian or symmetric diagonally dominant M-matrix…
Thresholded Local Hyper-Flow Diffusion
Meher Chaitanya, Sebastian Dalleiger, Luana Ruiz
Local Hyper-Flow Diffusion (HFD) gives an edge-size-independent Cheeger-type guarantee for seeded clustering in general submodular hypergraphs, but existing HFD solvers do not keep…
Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning
Meher Chaitanya, My Le, Luana Ruiz
We introduce Graph Cascades, a mesoscopic rewiring strategy for Graph Neural Networks (GNNs) and Graph Transformers (GTs) that captures intermediate-scale graph structure beyond pu…
Tight Sampling in Unbounded Networks
Kshitijaa Jaglan, Meher Chaitanya, Triansh Sharma +4
The default approach to deal with the enormous size and limited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbound…