3 papers
cs.LG2026
Understanding Truncated Positional Encodings for Graph Neural Networks
James Flora, Mitchell Black, Weng-Keen Wong +1
Positional encodings (PEs) enhance the power of graph neural networks (GNNs), both theoretically and empirically. Two of the most popular families of PEs - spectral (e.g., Laplacia…
cs.DS2025
Graph Inference with Effective Resistance Queries
Huck Bennett, Mitchell Black, Amir Nayyeri +1
The goal of graph inference is to design algorithms for learning properties of a hidden graph using queries to an oracle that returns information about the graph. Graph reconstruct…
cs.SI2025
Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering
Mitchell Black, Lucy Lin, Amir Nayyeri +1
Effective resistance is a distance between vertices of a graph that is both theoretically interesting and useful in applications. We study a variant of effective resistance called…