Author

Lakshman Tamil

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#machine learning Preprint Oct 2026

EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks

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 across epochs can require repeated sampling or recomputation. We introduce EDiS (Edge-Disjoint Subgraph sparsification framework), which se...

S. Navuluru, Siddhartha Shankar Das, Franck Dernoncourt et al. · 0 citations

#machine learning Preprint Sep 2026

Scaffold: Support Graph Theory Based Sparsification for Graph Neural Networks

To the authors' knowledge, Scaffold is the first scalable GNN sparsification framework to use a joint supporting-path dilation-congestion criterion, and across 19 homophilic and heterophilic benchmarks, Scaffold achieves the best aggregate rank among the evaluated sparsification and related methods.

Siddhartha Shankar Das, S. Navuluru, S. M. Ferdous et al. · 0 citations

Preprint Aug 2026

Unifying Graph Neural Networks Through a Common Layer Equation

A common layer equation is introduced that represents covered architectures through seven components: an update domain, channel set, propagation bank, per-channel message maps, channel-fusion operator, ego/residual map, and update map, which exposes the empirical inverse problem of mapping measurable graph and task pro...

S. Navuluru, Siddhartha Shankar Das, B. Ni et al. · 0 citations

#federated learning Preprint Aug 2026

A Federated Learning Framework for Privacy-Preserving Oral Cancer Screening on Smartphones

This work presents a practical FL framework that supports geographically distributed collaboration among AI healthcare researchers and facilitates the development of robust models for oral cancer screening.

Lena D. Swamikannan, A. Sonawane, J. Patel et al. · 0 citations