Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Authors: Robin Winter, Frank Noe, Djork-Arné Clevert
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our proposed model for graph reconstruction, generation and interpolation and evaluate the expressive power of extracted representations for downstream graph-level classification and regression. |
| Researcher Affiliation | Collaboration | Robin Winter Bayer AG Freie Universität Berlin robin.winter@bayer.com Frank Noé Freie Universität Berlin frank.noe@fu-berlin.de Djork-Arné Clevert Bayer AG djork-arne.clevert@bayer.com |
| Pseudocode | No | The paper does not contain an explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Code available at https://github.com/jrwnter/pigvae |
| Open Datasets | Yes | We perform experiments on synthetically generated graphs and molecular graphs from the public datasets QM9 and Pub Chem. ... Erdos-Renyi graphs [47], ... Barabasi-Albert graphs [48], ... and Ego graphs. ... QM9 dataset [54, 55]. This datasets contains about 134 thousand organic molecules... We extracted organic molecules with up to 32 heavy atoms, resulting into a set of approximately 67 million compounds (more details in Appendix F) from the public Pub Chem database [58]. |
| Dataset Splits | Yes | For all datasets we split the dataset into 80% train and 20% test data. For the QM9 dataset, we further split the train data into 90% train and 10% validation data. (From Appendix C) |
| Hardware Specification | Yes | Trainings were performed on a single GPU (NVIDIA A100-SXM4-40GB). (From Appendix C) |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and provides its hyperparameters but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For all experiments, we used a batch size of 32, a learning rate of 10−4 and Adam optimizer (β1 = 0.9, β2 = 0.999). We train for 200 epochs on synthetic data and for 100 epochs on QM9 dataset. (From Appendix C) |