Provably expressive temporal graph networks
Authors: Amauri Souza, Diego Mesquita, Samuel Kaski, Vikas Garg
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | PINT significantly outperforms existing TGNs on several real-world benchmarks.Our contributions are three-fold:extensive empirical investigations underscore practical benefits of this work. The proposed method is either competitive or significantly better than existing models on several real benchmarks for dynamic link prediction, in transductive as well as inductive settings. |
| Researcher Affiliation | Collaboration | 1Aalto University 2Getulio Vargas Foundation 3University of Manchester 4Yai Yai Ltd |
| Pseudocode | No | The paper does not contain a structured pseudocode or algorithm block. |
| Open Source Code | Yes | We run experiments using Py Torch [25] and code is available at www.github.com/Aalto PML/PINT. |
| Open Datasets | Yes | We use six popular benchmark datasets: Reddit, Wikipedia, Twitter, UCI, Enron, and Last FM [16, 27, 38, 42]. Notably, UCI, Enron, and Last FM are non-attributed networks, i.e., they do not contain feature vectors associated with the events. Node features are absent in all datasets, thus following previous works we set them to vectors of zeros [27, 42]. Since Twitter is not publicly available, we follow the guidelines by Rossi et al. [27] to create our version. We provide more details regarding datasets in the supplementary material. |
| Dataset Splits | Yes | We follow Xu et al. [42] and use a 70%-15%-15% (train-val-test) temporal split for all datasets. |
| Hardware Specification | No | The paper mentions 'computational resources provided by the Aalto Science-IT Project from Computer Science IT' but does not specify any exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions 'We run experiments using Py Torch [25]' but does not provide a specific version number for PyTorch or any other ancillary software components. |
| Experiment Setup | No | The paper states: 'We provide detailed information about hyperparameters and the training of each model in the supplementary material.' This indicates that specific experimental setup details, such as hyperparameter values, are not present in the main text of the paper. |