Graph Filtration Learning
Authors: Christoph Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that this type of readout operation compares favorably to previous techniques, especially when the graph connectivity structure is informative for the learning problem. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Salzburg, Austria 2Department of Biosystems Science and Engineering, ETH Zurich, Switzerland 3UNC Chapel Hill |
| Pseudocode | No | The paper describes the process and method in prose and through diagrams (Figure 1), but it does not include a dedicated pseudocode block or algorithm listing. |
| Open Source Code | Yes | Source code is publicly available at https://github.com/c-hofer/graph_filtration_learning. |
| Open Datasets | Yes | We use two common benchmark datasets for graphs with discrete node attributes, i.e., PROTEINS and NCI1, as well as four social network datasets (IMDB-BINARY, IMDB-MULTI, REDDIT-BINARY, REDDIT-5k) which do not contain any node attributes (see supplementary material). |
| Dataset Splits | Yes | For evaluation, we follow previous work (see, e.g., Morris et al., 2019; Zhang et al., 2018a) and report cross-validation accuracy, averaged over ten folds, of the model obtained in the final training epoch. |
| Hardware Specification | No | The paper mentions running computations on a "parallel GPU variant" in section 5.2, but it does not specify the model or type of GPU, or any other specific hardware components like CPU or memory. |
| Software Dependencies | No | The paper states that the implementation uses "PyTorch", but it does not specify a version number for this or any other software dependency. |
| Experiment Setup | Yes | We train for 100 epochs using ADAM with an initial learning rate of 0.01 (halved every 20th epoch) and a weight decay of 10^-6. No hyperparameter tuning or early stopping is used. |