Solving graph compression via optimal transport
Authors: Vikas Garg, Tommi Jaakkola
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted several experiments to demonstrate the merits of our method.Table 1: Description of graph datasets, and comparison of accuracy on test data. |
| Researcher Affiliation | Academia | Vikas K. Garg CSAIL, MIT vgarg@csail.mit.eduTommi Jaakkola CSAIL, MIT tommi@csail.mit.edu |
| Pseudocode | Yes | Algorithm 1 Algorithm to compute Euclidean projection on the d-simplex under a diagonal transformation. Algorithm 2 Mirror Prox algorithm to (approximately) find ϵ in relaxation of (9). |
| Open Source Code | No | No explicit statement or link providing access to the authors' own source code was found. |
| Open Datasets | Yes | We used several standard graph datasets for our experiments, namely, DHFR [51], BZR-MD [52], MSRC-9, MSRC-21C [53], and Mutagenicity [54]. |
| Dataset Splits | Yes | We partitioned each dataset into multiple train and test sets of varying sizes. Specifically, for each p {0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}, we divided each dataset randomly into a train set containing a fraction p of the graphs in the dataset, and a test set containing the remaining 1 p fraction. To mitigate the effects of chance, we formed 5 such independent train-test partitions for each fraction p for each dataset. For each method and each train-test split, we used a separate 5-fold cross-validation procedure to tune the coefficient of error term C over the set {0.1, 1, 10} for training an independent SVM model on the training portion. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory, or cluster specifications) used for running experiments were found in the paper. |
| Software Dependencies | No | The paper mentions software like SVMs and the Weisfeiler-Leman subtree kernel, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We fixed the value of hyperparameters in Algorithm 2 for all our experiments. Specifically, we set the regularization coefficient λ = 1, and the gradient rates αℓ= 0.1, βℓ= 0.1, γℓ= 0.1 for each ℓ {0, 1, . . . , T}. We also let ρ0 be the stationary distribution by setting ρ0(v) for each v V as the ratio of deg(v), i.e. the degree of v, to the sum of degrees of all the vertices. We also fixed T = 25 for our algorithm. For each method and each train-test split, we used a separate 5-fold cross-validation procedure to tune the coefficient of error term C over the set {0.1, 1, 10} for training an independent SVM model on the training portion. |