Hierarchical Graph Representation Learning with Differentiable Pooling
Authors: Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, Jure Leskovec
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that combining existing GNN methods with DIFFPOOL yields an average improvement of 5−10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark data sets. |
| Researcher Affiliation | Academia | Rex Ying rexying@stanford.edu Stanford University Jiaxuan You jiaxuan@stanford.edu Stanford University Christopher Morris christopher.morris@udo.edu TU Dortmund University Xiang Ren xiangren@usc.edu University of Southern California William L. Hamilton wleif@stanford.edu Stanford University Jure Leskovec jure@cs.stanford.edu Stanford University |
| Pseudocode | No | The paper describes the proposed method using mathematical equations and descriptions, but it does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We use protein data sets including ENZYMES, PROTEINS [3, 12], D&D [10], the social network data set REDDIT-MULTI-12K [39], and the scientific collaboration data set COLLAB [39]. |
| Dataset Splits | Yes | For all these data sets, we perform 10-fold cross-validation to evaluate model performance, and report the accuracy averaged over 10 folds. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions various software components and models like GRAPHSAGE, GCN, LIBSVM, but does not provide specific version numbers for these or other ancillary software dependencies. |
| Experiment Setup | Yes | In our experiments, the GNN model used for DIFFPOOL is built on top of the GRAPHSAGE architecture... A total of 2 DIFFPOOL layers are used for the datasets... After each DIFFPOOL layer, 3 layers of graph convolutions are performed... In the 2 DIFFPOOL layer architecture, the number of clusters is set as 25% of the number of nodes... while in the 1 DIFFPOOL layer architecture, the number of clusters is set as 10%. Batch normalization [18] is applied after every layer of GRAPHSAGE. We also found that adding an ℓ2 normalization to the node embeddings at each layer made the training more stable. All models are trained for 3 000 epochs with early stopping applied when the validation loss starts to drop. |