Decoupling the Depth and Scope of Graph Neural Networks
Authors: Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, on seven graphs (with up to 110M nodes) and six backbone GNN architectures, our design achieves significant accuracy improvement with orders of magnitude reduction in computation and hardware cost. |
| Researcher Affiliation | Collaboration | Hanqing Zeng USC zengh@usc.edu Muhan Zhang Peking University, BIGAI muhan@pku.edu.cn Yinglong Xia Facebook AI yxia@fb.com Ajitesh Srivastava USC ajiteshs@usc.edu Andrey Malevich Facebook AI amalevich@fb.com Rajgopal Kannan US ARL rajgopal.kannan.civ@mail.mil Viktor Prasanna USC prasanna@usc.edu Long Jin Facebook AI longjin@fb.com Ren Chen Facebook AI renchen@fb.com |
| Pseudocode | Yes | See Appendix D and F.3 for algorithm and experiments. |
| Open Source Code | Yes | Our code is available at https://github.com/facebookresearch/shaDow_GNN |
| Open Datasets | Yes | We evaluate SHADOW-GNN on seven graphs. Six of them are for the node classification task: Flickr [55], Reddit [12], Yelp [55], ogbn-arxiv, ogbn-products and ogbn-papers100M [16]. |
| Dataset Splits | Yes | We follow the default data splits for all datasets, which are usually 60% training, 20% validation, and 20% test. For ogbn-papers100M, the training, validation, test splits are 80%, 10%, 10% respectively. (from Appendix E.1) |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] |
| Software Dependencies | No | The paper mentions using W&B [5] but does not provide specific version numbers for software dependencies or libraries in the text. |
| Experiment Setup | Yes | All models on all datasets have uniform hidden dimension of 256. [...] For the model depth, since L = 3 is the standard setting in the literature (e.g., see the benchmarking in OGB [16]), we start from L = 3 and further evaluate a deeper model of L = 5. Hyperparameter tuning and architecture configurations are in Appendix E.4. (Appendix E.4 specifies: 'hidden dimension of 256 for all models', 'learning rate of 0.001', 'Adam optimizer [21] with weight decay of 5e-4', 'number of training epochs is 1000', 'dropout rate set to 0.5') |