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..
Graph Edit Distance Learning via Modeling Optimum Matchings with Constraints
Authors: Yun Peng, Byron Choi, Jianliang Xu
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that our method is 4.2x-103.8x more accurate than the state-of-the-art methods on real-world benchmark graphs. |
| Researcher Affiliation | Academia | Yun Peng , Byron Choi , Jianliang Xu Hong Kong Baptist University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available online.1 1https://github.com/csypeng/graph edit distance learning |
| Open Datasets | Yes | We use four real graph datasets AIDS, IMDB, LINUX and PTC that are from different domains in our experiments. The datasets are the same as those used in [Bai et al., 2020]. |
| Dataset Splits | Yes | To answer Q1 with this experiment, we sample 6k, 2k, 2k pairs of graphs in G 30 as the training data, the validation data and the test data, respectively. |
| Hardware Specification | Yes | The experiments are conducted using Py Torch on a server with Intel CPU Xeon Gold 6230R, 768G RAM, and a GPU card NVIDIA Tesla K80. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Adam optimizer' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We use two graph convolution layers and Re LU as the activation function. We use the one-hot encoding of node degree as the initial node embedding. The embedding dimensions are 32. ... We set the batch size to 1 and use the Adam optimizer. The initial learning rate is 0.005 and reduced by 0.96 for each 5 epochs. We set the number of epochs to 600, and select the best model based on the lowest MSE of GED on validation data. |