Author

B. Coskunuzer

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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

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 a natural way to reduce these costs, but removing edges indiscriminately can distort important communication structure and degrade predictiv...

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

#artificial intelligence Preprint Sep 2026

TopU-LBVS: A Realistic Multi Target Benchmark for Ligand Based Virtual Screening

TopU-LBVS is introduced, a multi-target benchmark for LBVS under hard-negative screening conditions, and fixed splits, evaluation code, and baseline implementations are released for reproducible comparison of future LBVS and molecular representation learning methods.

Surbhi Kumar, Yu-He Zhou, V. Shiralkar et al. · 0 citations

#machine learning Preprint Sep 2026

Heat Field Signatures: From Point Clouds to Smooth Geometry

HFS turns a classical heat field into a practical interface for multiscale geometric analysis in modern point-cloud learning, and introduces the Heat Dimension Spectrum (HDS), a compact summary of multiscale geometric composition.

Yuan-Qing Wang, Ya-Peng Tian, B. Coskunuzer · 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

Jul 2026

Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors

The CoTask Score (CTS) is introduced to summarize joint NC+LP utility when a shared encoder must serve both tasks, and it is shown that simple dataset statistics can guide mechanism choice and help avoid negative transfer.

Neelam Akula, Surbhi Kumar, Murat Kantarcioglu et al. · arXiv.org · 0 citations

Jul 2026

TopoFormer: Topology Meets Attention for Graph Learning

This work introduces Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences by decomposing a graph into a short, ordered sequence of topological tokens by slicing over node or edge filtrations.

Md Joshem Uddin, Astrit Tola, C. Akcora et al. · arXiv.org · 1 citation