Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Boosting Graph Pooling with Persistent Homology
Authors: Chaolong Ying, Xinjian Zhao, Tianshu Yu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we apply our mechanism to a collection of graph pooling methods and observe consistent and substantial performance gain over several popular datasets, demonstrating its wide applicability and flexibility. In the experiments, we evaluate the benefits of persistent homology on several state-of-the-art graph pooling methods, with the goal of answering the following questions: |
| Researcher Affiliation | Academia | Chaolong Ying, Xinjian Zhao, Tianshu Yu School of Data Science, The Chinese University of Hong Kong, Shenzhen EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods and processes through text and equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is open-sourced at https://github.com/LOGO-CUHKSZ/TIP.git. |
| Open Datasets | Yes | To evaluate the capabilities of our model across diverse domains, we assess its performance on a variety of graph datasets commonly used in graph related tasks. We select several benchmarks from TU datasets [32], OGB datasets [20] and ZINC dataset [43]. We use the default dataset settings from Py G library 3. |
| Dataset Splits | Yes | In the graph classification task, all datasets are splitted into train (80%), validation (10%), and test (10%) data. |
| Hardware Specification | Yes | The experiments are conducted using an AMD EPYC 7542 CPU and a single NVIDIA 3090 GPU. |
| Software Dependencies | No | The paper mentions “All the methods are implemented using Py Torch and Py G [37, 9].” but does not specify version numbers for PyTorch or PyG, which are crucial for reproducibility. |
| Experiment Setup | Yes | Hyperparameters. For dense pooling methods, the pooling ratio ranges from [0.1, 0.5], the number of pooling layers is 2, and the hidden dimension is selected from {32, 64}. For the Graclus method we use 2 pooling layers, while for Top K we use 3 pooling layers with a pooling ratio of 0.8. The batch size for all models is uniformly set to 20, and the maximum number of training epochs is 1000. Following the evaluation protocol in [50, 30], we train all models using the Adam optimizer [24] and implement a learning rate decay mechanism, reducing the learning rate from 10 3 to 10 5 with a decay ratio of 0.5 and a patience of 10 epochs. Additionally, we use early stopping based on the validation accuracy with patience of 50 epochs. |