Path Integral Based Convolution and Pooling for Graph Neural Networks
Authors: Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, Pietro Liò
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the test results of PAN on various datasets in graph classification tasks. We show a performance comparison of PAN with some existing GNN methods. All the experiments were performed using Py Torch Geometric [22] and run on a server with Intel(R) Core(TM) i9-9820X CPU 3.30GHz, NVIDIA Ge Force RTX 2080 Ti and NVIDIA TITAN V GV100. The codes can be downloaded at https://github.com/Yu Guang Wang/PAN. |
| Researcher Affiliation | Academia | Zheng Ma Department of Physics Princeton University mazhengparnassum@gmail.com Junyu Xuan Centre for Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney junyu.xuan@uts.edu.au Yu Guang Wang Max Planck Institute for Mathematics in the Sciences & School of Mathematics and Statistics University of New South Wales yuguang.wang@mis.mpg.de Ming Li Department of Educational Technology Zhejiang Normal University mingli@zjnu.edu.cn Pietro Liò Department of Computer Science and Technology University of Cambridge Pietro.Lio@cl.cam.ac.uk |
| Pseudocode | No | The paper describes the proposed methods in narrative text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes can be downloaded at https://github.com/Yu Guang Wang/PAN. |
| Open Datasets | Yes | We test the performance of PAN on five widely used benchmark datasets for graph classification tasks [33], including two protein graph datasets PROTEINS and PROTEINS_full [10, 19]; one mutagen dataset MUTAGEN [50, 32] (full name Mutagenicity); and one dataset that consists of chemical compounds screened for activity against non-small cell lung cancer and ovarian cancer cell lines NCI1 [56]; one dataset that consists of molecular compounds for activity against HIV or not AIDS [50]. We name the dataset Point Pattern, which can be downloaded from the links contained in the supplementary material. |
| Dataset Splits | Yes | In each experiment, we split 80%, 10%, and 10% of each dataset for training, validation, and testing. We split the data into training, validation, and test sets of size 12,000, 1,500, and 1,500. |
| Hardware Specification | Yes | All the experiments were performed using Py Torch Geometric [22] and run on a server with Intel(R) Core(TM) i9-9820X CPU 3.30GHz, NVIDIA Ge Force RTX 2080 Ti and NVIDIA TITAN V GV100. |
| Software Dependencies | No | All the experiments were performed using Py Torch Geometric [22]. The paper mentions a software library but does not provide specific version numbers for it or other ancillary software. |
| Experiment Setup | Yes | We use batch size of 128 and 200 epochs. The learning rate and weightdecay are set to 0.01 and 5e-4, respectively. We fix the number of neurons in the convolutional layers to 64, the learning rate and weight decay are set to 0.001 and 0.0005. |