A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
Authors: Yifan Chen, Rentian Yao, Yun Yang, Jie Chen
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The study includes a set of experiments to support the theory and method, including approximating the GW distance, preserving the graph spectrum, classifying graphs using spectral information, and performing regression using graph convolutional networks.Code is available at https: //github.com/ychen-stat-ml/ GW-Graph-Coarsening. |
| Researcher Affiliation | Collaboration | 1Hong Kong Baptist University 2University of Illinois Urbana Champaign 3MIT-IBM Watson AI Lab, IBM Research. |
| Pseudocode | Yes | Algorithm 1 Kernel graph coarsening (KGC). |
| Open Source Code | Yes | Code is available at https: //github.com/ychen-stat-ml/ GW-Graph-Coarsening. |
| Open Datasets | Yes | We evaluate graph coarsening methods, including ours, on eight benchmark graph datasets: MUTAG (Debnath et al., 1991; Kriege & Mutzel, 2012), PTC (Helma et al., 2001), PROTEINS (Borgwardt et al., 2005; Schomburg et al., 2004), MSRC (Neumann et al., 2016), IMDB (Yanardag & Vishwanathan, 2015), Tumblr (Oettershagen et al., 2020), AQSOL (Sorkun et al., 2019; Dwivedi et al., 2020), and ZINC (Irwin et al., 2012). |
| Dataset Splits | Yes | They apply a scaffold splitting (Hu et al., 2020) to the AQSOL dataset in the ratio 8 : 1 : 1 to have 7831, 996, and 996 samples for train, validation, and test sets. |
| Hardware Specification | Yes | The algorithms tested are all implemented in unoptimized Python code, and run with one core of a server CPU (Intel(R) Xeon(R) Gold 6240R CPU @ 2.40GHz) on Ubuntu 18.04. |
| Software Dependencies | No | The paper mentions 'unoptimized Python code' and 'Ubuntu 18.04'. It also mentions the 'OT package POT (Flamary et al., 2021, Python Optimal Transport)'. However, it does not specify version numbers for Python, POT, or any other libraries used. |
| Experiment Setup | Yes | For the learning rate strategy across all GCN models, we follow the existing setting to choose the initial learning rate as 1 10 3, the reduce factor is set as 0.5, and the stopping learning rate is 1 10 5. Also, all the GCN models tested in our experiments share the same architecture the network has 4 layers and 108442 tunable parameters. |