Graph Edit Distance with General Costs Using Neural Set Divergence
Authors: Eeshaan Jain, Indradyumna Roy, Saswat Meher, Soumen Chakrabarti, Abir De
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 eeshaan.jain@epfl.ch {saswatmeher,soumen,indraroy15,abir}@cse.iitb.ac.in |
| 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. |