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..
Graph Filtration Learning
Authors: Christoph Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt
ICML 2020 | Venue PDF | 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. |