12 citations · 109 across the 50 of their papers we have counts for
8 papers · 1 filter
EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks
Sai Karthik Navuluru, Siddhartha Shankar Das, Franck Dernoncourt +7
Sparse GNN training reduces computation, but deciding which edges to keep can be costly. Reusing one sparse graph is cheap, but locks training to a fixed topology, while varying it…
Scaffold: Support Graph Theory Based Sparsification for Graph Neural Networks
Siddhartha Shankar Das, Sai Karthik Navuluru, S M Ferdous +7
Graph neural networks (GNNs) rely on message passing over graph edges, making their computational and memory costs strongly dependent on graph density. Graph sparsification offers…
TopU-LBVS: A Realistic Multi Target Benchmark for Ligand Based Virtual Screening
Surbhi Kumar, Yuhe Zhou, Varun Shiralkar +2
Ligand-based virtual screening (LBVS) is a practical first-pass tool in early-stage drug discovery, but existing benchmarks can overestimate performance through random negatives, e…
Heat Field Signatures: From Point Clouds to Smooth Geometry
Yuanqing Wang, Yapeng Tian, Baris Coskunuzer
Bringing multiscale geometric analysis directly to irregular point clouds remains difficult: quantities such as local dimension, anisotropy, density variation, and geometric transi…
Unifying Graph Neural Networks Through a Common Layer Equation
Sai Karthik Navuluru, Siddhartha Shankar Das, Bo Ni +9
Graph neural networks are commonly described through family-specific equations whose notation obscures shared computations and structural differences. We introduce a common layer e…
Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
Neelam Akula, Surbhi Kumar, Murat Kantarcioglu +1
Many real-world graphs support multiple predictive tasks over the same underlying structure, creating an opportunity to reuse supervision across node classification (NC) and link p…