Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
Authors: Takanori Maehara, Hoang NT
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we develop a theoretical framework for graph classification problems in the partial observation setting (i.e., subgraph samplings). Equipped with insights from graph limit theory, we propose a new graph classification model that works on a randomly sampled subgraph and a novel topology to characterize the representability of the model. Our theoretical framework contributes a theoretical validation of mini-batch learning on graphs and leads to new learning-theoretic results on generalization bounds as well as size-generalizability without assumptions on the input. |
| Researcher Affiliation | Collaboration | Takanori Maehara Facebook AI London, United Kingdom EMAIL Hoang NT Tokyo Tech & RIKEN AIP Tokyo, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1 Randomized Benjamini Schramm GNN |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not report on experiments with specific datasets; therefore, no public dataset access information is provided. |
| Dataset Splits | No | This is a theoretical paper and does not report on experiments; therefore, no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | This is a theoretical paper and does not report on experiments; therefore, no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper and does not report on experiments; therefore, no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | This is a theoretical paper and does not report on experiments; therefore, no specific experimental setup details or hyperparameters are provided. |