From the 1 of 6 linked papers with an AI index.
6 papers
Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization
Robert Jankowski, Pedro Almagro-Blanco, Marián Boguñá +2
The paper proposes training graph neural networks on geometrically renormalized, coarse‑grained versions of a graph and then directly applying the learned weights to the original f…
Powers of the Vandermonde determinant are eventually non-SNP
Thien Le, Melanie Weber
We prove a conjecture of Monical, Tokcan, and Yong that every fixed positive power of the Vandermonde determinant is non-SNP in all sufficiently many variables, where a polynomial…
Contrastive Neural Algorithmic Reasoning for Graph Coloring
Thien Le, Tianyu Zhao, Melanie Weber
Graph coloring seeks to assigns colors to a graph's nodes so that adjacent nodes receive different colors, using as few colors as possible. Here, we study approximate -coloring,…
Towards Distillation Guarantees under Algorithmic Alignment for Combinatorial Optimization
Thien Le, Melanie Weber
Distillation transfers knowledge from a large model trained on broad data to a smaller, more efficient model suitable for deployment. In structured prediction settings, prior knowl…
Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing
Lukas Fesser, Melanie Weber
Graph Neural Networks have emerged as the most popular architecture for graph-level learning, including graph classification and regression tasks, which frequently arise in areas s…
Enhancing the Utility of Higher-Order Information in Relational Learning
Raphael Pellegrin, Lukas Fesser, Melanie Weber
Higher-order information is crucial for relational learning in many domains where relationships extend beyond pairwise interactions. Hypergraphs provide a natural framework for mod…