Structural Entropy Guided Graph Hierarchical Pooling
Authors: Junran Wu, Xueyuan Chen, Ke Xu, Shangzhe Li
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results show that SEP outperforms state-of-the-art graph pooling methods on graph classification benchmarks and obtains superior performance on node classifications. |
| Researcher Affiliation | Academia | 1State Key Lab of Software Development Environment, Beihang University, Beijing, 100191, China 2School of Mathematical Science, Beihang University, Beijing, 100191, China. Correspondence to: Shangzhe Li <shangzheli@buaa.edu.cn>. |
| Pseudocode | Yes | Algorithm 1 Coding tree with height k via structural entropy minimization Input: a graph G = (V, E), a positive integer k > 1 Output: a coding tree T with height k |
| Open Source Code | Yes | The implementation of Algorithm 1, SEP-G and SEP-N can be found at https://github.com/Wu-Junran/SEP. |
| Open Datasets | Yes | Seven benchmarks for graph classification are selected from TU datasets (Morris et al., 2020). Specifically, we employ three social network datasets, including IMDB-BINARY, IMDB-MULTI, and COLLAB; and four bioinformatics datasets, including MUTAG, PROTEINS, D&D and NCI1. We evaluate SEP-N under the transductive learning setting, which includes three datasets Cora, Citeseer and Pubmed (Sen et al., 2008). |
| Dataset Splits | Yes | Following (Xu et al., 2019; Lee et al., 2019), 10-fold cross-validation is conducted, and we present the average accuracies achieved to validate the performance of SEP-G in graph classification. In addition, we use the 10 percent of the training data as a validation data following the fair comparison setup (Errica et al., 2020). The designated training/validation/testing splits on Cora, Citeseer and Pubmed are adopted, that is, training set has 20 nodes for each class, validation set has 500 nodes and testing set has 1,000 nodes. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | For model configuration, the pooling ratio of all models is set to 25%, the learning rate is set to 5 × 10^-3, and hidden size is set to 32. For model configuration, the learning rate is set to 5 × 10^-4, the hidden size is set {64, 128}, the batch size is set {32, 128}, weight decay is set to 1 × 10^-4, and dropout rate is set {0, 0.5}. For model configuration, the learning rate is set to 0.01, the hidden size is set {16, 32, 128, 256}, weight decay is set {0.02, 5 × 10^-4}, and dropout rate for each layer is set {0, . . . , 0.9}. |