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..
Size-Invariant Graph Representations for Graph Classification Extrapolations
Authors: Beatrice Bevilacqua, Yangze Zhou, Bruno Ribeiro
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conclude with synthetic and real-world dataset experiments showcasing the bene๏ฌts of representations that are invariant to train/test distribution shifts. |
| Researcher Affiliation | Academia | 1Department of Computer Science, and 2Department of Statistics, Purdue University, West Lafayette, Indiana, USA. |
| Pseudocode | No | The paper describes methods and equations, but does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Our code is also available1. 1https://github.com/Purdue MINDS/ size-invariant-GNNs |
| Open Datasets | Yes | We use the f MRI brain graph data on 71 schizophrenic patients and 74 controls for classifying individuals with schizophrenia (De Domenico et al., 2016). We consider four vertex-attributed datasets (NCI1, NCI109, DD, PROTEINS) from Morris et al. (2020), and split the data as proposed by Yehudai et al. (2021). |
| Dataset Splits | Yes | For each task, we report (a) training accuracy (b) validation accuracy, which are new examples sampled from P(Y, Gtr Ntr); and (c) extrapolation test accuracy... We employ a 5-fold cross-validation for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using PyTorch Geometric and PyTorch, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The graph representations are then passed to a L-hidden layer feedforward neural network (MLP) with softmax outputs that give the predicted classes, L {0, 1}. For practical reasons, we focus only on densities of graphs of size exactly k, which is treated as a hyperparameter. We employ a 5-fold cross-validation for hyperparameter tuning. |