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..
KerGM: Kernelized Graph Matching
Authors: Zhen Zhang, Yijian Xiang, Lingfei Wu, Bing Xue, Arye Nehorai
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate our approach, and show that our algorithm significantly outperforms the state-of-the-art in both matching accuracy and scalability. |
| Researcher Affiliation | Collaboration | 1Washington University in St. Louis 2IBM Research |
| Pseudocode | Yes | Algorithm 1 The En FW optimization algorithm for minimizing Fα (15) |
| Open Source Code | No | The paper states "We implement all the algorithms using Matlab" but provides no information about public availability of the source code for the described methodology. |
| Open Datasets | Yes | The CMU House Sequence dataset has 111 frames of a house, each of which has 30 labeled landmarks. The Pascal dataset [26] has 20 pairs of motorbike images and 30 pairs of car images. The S.cerevisiae (yeast) PPI network [7] dataset is popularly used to evaluate PPI network aligners because it has known true node correspondences. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test splits used for its experiments. It evaluates "matching accuracy" on various datasets and pairs, but does not describe a formal data splitting process for training, validation, and testing. |
| Hardware Specification | Yes | We implement all the algorithms using Matlab on an Intel i7-7820HQ, 2.90 GHz CPU with 64 GB RAM. |
| Software Dependencies | No | The paper states "We implement all the algorithms using Matlab" but does not specify a version number for Matlab or any other software dependencies. |
| Experiment Setup | Yes | For our method, if not specified, we set the regularization parameter (see (15)) λ = 0.005 and the path following parameters α = 0 : 0.1 : 1. We use the Hungarian algorithm for final discretization. We set the parameter γ = 5 and the dimension D = 20. |