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..
COPT: Coordinated Optimal Transport on Graphs
Authors: Yihe Dong, Will Sawin
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, COPT outperforms state of the art methods in graph classification on both synthetic and real datasets. |
| Researcher Affiliation | Collaboration | Yihe Dong Microsoft Will Sawin Department of Mathematics Columbia University |
| Pseudocode | Yes | Algorithm 1 COPT graph sketching and graph distance |
| Open Source Code | No | The paper does not provide concrete access to source code (e.g., a specific repository link or an explicit code release statement) for the methodology described. |
| Open Datasets | Yes | This is done on four benchmark datasets over diverse domains: Proteins [6], BZR_MD [26], MSRC_9 [38], and Enzymes [45]. |
| Dataset Splits | Yes | The SVM is trained with parameters found using 3-fold cross validation on the training set, using a fast approximation of the multiscale Laplacian kernel (using the Nyström Method [53]). |
| Hardware Specification | Yes | Our implemention uses Py Torch and one P100 GPU, on a 2.60GHz six-core Intel CPU machine. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Gradient descent is used to minimize the analytic formulation Eq (5), where L Y and P are updated at each step, with the Adam optimizer [23] with a multistep learning rate scheduler that reduces the learning rate multiplicatively at regular intervals. |