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..
A Few Moments Please: Scalable Graphon Learning via Moment Matching
Authors: Reza Ramezanpour, Victor Manuel Tenorio Gomez, Antonio G. Marques, Ashutosh Sabharwal, Santiago Segarra
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our graphon estimation method achieves strong empirical performance demonstrating high accuracy on small graphs and superior computational efficiency on large graphs outperforming state-of-the-art scalable estimators in 75% of benchmark settings and matching them in the remaining cases. Furthermore, Moment Mixup demonstrated improved graph classification accuracy on the majority of our benchmarks. |
| Researcher Affiliation | Academia | Reza Ramezanpour Rice University EMAIL Victor M. Tenorio King Juan Carlos University EMAIL Antonio G. Marques King Juan Carlos University EMAIL Ashutosh Sabharwal Rice University EMAIL Santiago Segarra Rice University EMAIL |
| Pseudocode | Yes | The pseudocode of Moment Mixup is provided in Algorithm 1 in Appendix G. |
| Open Source Code | Yes | Source Code: https://github.com/rezar76/Graphon-Moment-Matching |
| Open Datasets | Yes | To evaluate the performance of our Moment Mixup framework, we conducted graph classification experiments on several real-world datasets: AIDS [27], IMDB-Binary [39], IMDB-Multi [39], and Reddit-Binary [39]. |
| Dataset Splits | Yes | To ensure a fair comparison with prior work, we adopted the same data splitting methodology as reported in previous literature [3, 13]. |
| Hardware Specification | Yes | The primary deep learning components of our experiments were executed on an Nvidia A100 GPU. Separately, empirical graph moments were computed using the ORCA toolkit [15], with its execution parallelized across an AMD EPYC 7742 64-Core Processor. |
| Software Dependencies | No | The paper mentions using the "ORCA toolkit [15]" but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | Yes | We use L = 20000, which is the number of samples to compute the density of moments of Moment Net using Eq. 4 in both experiments. We treated the INR architecture as a hyperparameter to account for function complexity, noting that a simple one-layer MLP [17] with 64 neurons sufficed for non-complex graphons, while more complex cases (like Stochastic Block Models) required architectures such as SIREN [29] to accurately represent high-frequency details. For data augmentation, we treated αmix, Nnodes, Ngraph, and Nsample as hyperparameters in Algorithm 1 and the best parameters are provided in Appendix K. |