Contrastive General Graph Matching with Adaptive Augmentation Sampling
Authors: Jianyuan Bo, Yuan Fang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 {jybo.2020, yfang}@smu.edu.sg |
| 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. |