arXiv Machine Learning By Soham Dan

Two-Sample Testing for Random Graphs without Vertex Correspondence

Read the original on arXiv Machine Learning →

arXiv:2610. 07503v1 Announce Type: cross Abstract: Two populations of graphs often have to be compared without any correspondence between their vertices, for instance when networks come from different communities, or when a graph generative model is evaluated against held-out graphs.

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arXiv AI
Sep 4

AutoGraphForge: Towards Automated Graph Theory Discovery

AutoGraphForge is a computational pipeline designed to automate the discovery, refutation, formalization, and proving of graph-theoretic conjectures. It generates conjectures using a Graffiti3 generator, filters out known results with a novelty filter, tests candidates against a large dataset of graphs, and refines surviving conjectures through counterexample search. The pipeline then translates each conjecture into Lean 4, verifies proofs with neural provers, and integrates the results into a formal library.

By J\'an Pastorek
arXiv Machine Learning
Jun 18

Robust Detection of Planted Subgraphs in Semi-Random Models

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By Dor Elimelech, Wasim Huleihel
arXiv Machine Learning
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Lost in Aggregation: On a Fundamental Expressivity Limit of Message-Passing Graph Neural Networks

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By Eran Rosenbluth