3 papers
cs.LG2026
NOMAD: Generating Embeddings for Massive Distributed Graphs
Aishwarya Sarkar, Sayan Ghosh, Nathan R. Tallent +1
Successful machine learning on graphs or networks requires embeddings that not only represent nodes and edges as low-dimensional vectors but also preserve the graph structure. Esta…
math.CO2026
On Directed Graphs with the Same Sum over Arborescence Weights
Sayani Ghosh, Bradley S. Meyer
We show that certain digraphs with the same vertex set but different arc sets have the same sum over the weights of all arborescences with a given root vertex. We relate our result…
cs.DC2024
MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs
Aishwarya Sarkar, Sayan Ghosh, Nathan R. Tallent +1
Graph Neural Networks (GNN) are indispensable in learning from graph-structured data, yet their rising computational costs, especially on massively connected graphs, pose significa…