COPT: Coordinated Optimal Transport on Graphs
Authors: Yihe Dong, Will Sawin
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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. |