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 with General Costs Using Neural Set Divergence
Authors: Eeshaan Jain, Indradyumna Roy, Saswat Meher, Soumen Chakrabarti, Abir De
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several datasets, under a variety of edit cost settings, show that GRAPHEDX consistently outperforms state-of-the-art methods and heuristics in terms of prediction error. |
| Researcher Affiliation | Academia | EPFL IIT Bombay EMAIL EMAIL |
| Pseudocode | Yes | In Algorithm 1, we present the pseudocode to generate the optimal edit path given the learnt node and edge alignments from GRAPHEDX. |
| Open Source Code | Yes | The code is available at https://github.com/structlearning/Graph Ed X. |
| Open Datasets | Yes | We experiment with seven real-world datasets: Mutagenicity (Mutag) [18], Ogbg-Code2 (Code2) [23], Ogbg-Molhiv (Molhiv) [23], Ogbg-Molpcba (Molpcba) [23], AIDS [36], Linux [5] and Yeast [36]. |
| Dataset Splits | Yes | We divide it into training, validation and test folds with a split ratio of 60:20:20. |
| Hardware Specification | Yes | The training of our models and the baselines was performed across servers containing Intel Xeon Silver 4216 2.10GHz CPUs, and Nvidia RTX A6000 GPUs. |
| Software Dependencies | Yes | We implement our models using Python 3.11.2 and Py Torch 2.0.0. |
| Experiment Setup | Yes | The following hyperparameters are used for training: Adam optimiser with a learning rate of 0.001 and weight decay of 0.0005, batch size of 256, early stopping with patience of 100 epochs, and Sinkhorn temperature set to 0.01. |