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..
Contrastive General Graph Matching with Adaptive Augmentation Sampling
Authors: Jianyuan Bo, Yuan Fang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the proposed model GCGM and Bi AS. ... We tested three real-world datasets. ... We assessed the performance of GCGM against diverse baselines, including supervised, learning-free, and unsupervised methods. |
| Researcher Affiliation | Academia | Jianyuan Bo , Yuan Fang Singapore Management University, Singapore EMAIL |
| Pseudocode | Yes | Pseudocode of our method can be found in Appendix A. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We tested three real-world datasets. (1) Pascal VOC [Bourdev and Malik, 2009; Everingham et al., 2010] includes images from 20 classes; (2) Willow [Cho et al., 2013] offers 256 images over five classes; (3) SPair-71k [Min et al., 2019] has 70,958 image pairs across 18 classes. Besides, we followed a recent work [Liu et al., 2023] to generate a synthetic dataset from random 2D node coordinates for the general non-visual domain. All graphs are constructed based on Delaunay triangulation. More dataset details are presented in Appendix C. |
| Dataset Splits | Yes | Our reported results for supervised methods and SCGM might be slightly lower than their original papers due to our 80:20 train-validation split from the original training set, as the original splits lack a validation set. We repeated the splits five times using varied random seeds. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'Think Match' and 'Optuna' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | For Bi AS, we set λ = 0.8, α = 3, |P| = 512. However, for the Willow dataset, due to its smaller size, we adjust |P| to 128. Early stopping was applied if performance improvements were below the threshold ϵ = 0.001. Detailed model and parameter configurations can be found in Appendix A. |