Size-Invariant Graph Representations for Graph Classification Extrapolations
Authors: Beatrice Bevilacqua, Yangze Zhou, Bruno Ribeiro
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conclude with synthetic and real-world dataset experiments showcasing the beneļ¬ts 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. |