Graph Coarsening with Neural Networks
Authors: Chen Cai, Dingkang Wang, Yusu Wang
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on both synthetic and real networks, we demonstrate that our method significantly improves common graph coarsening methods under various metrics, reduction ratios, graph sizes, and graph types. |
| Researcher Affiliation | Academia | University of California, San Diego, c1cai@ucsd.edu Ohio State University, wang.6150@osu.edu University of California, San Diego, yusuwang@ucsd.edu |
| Pseudocode | Yes | Algorithm 1: Iterative algorithm for edge weight optimization |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We use the following synthetic graphs: Erd os-R enyi graphs (ER), Barabasi-Albert Graph (BA), Watts-Strogatz Graph (WS), random geometric graphs (GEO). ... We test on five real networks: Shape, Pub Med, Coauthor-CS (CS), Coauthor-Physics (Physics), and Flickr (largest one with 89k vertices)... Pub Med (Sen et al., 2008) ... Flickr (Zeng et al., 2019) |
| Dataset Splits | Yes | We randomly sample 25 graphs of size {512, 612, 712, ..., 2912} from different generative models. If the graph is disconnected, we keep the largest component. We train GOREN on the first 5 graphs, use the 5 graphs from the rest 20 graphs as the validation set and the remaining 15 as test graphs. |
| Hardware Specification | Yes | All experiments are performed on a single Intel Xeon CPU E5-2630 v4@ 2.20GHz 40 and 64GB RAM machine. |
| Software Dependencies | No | The paper mentions software like Pytorch and Pytorch Geometric with citations, and Adam optimizer, but does not provide explicit version numbers for these software dependencies (e.g., PyTorch 1.x). |
| Experiment Setup | Yes | All models are trained with Adam optimizer with a learning rate of 0.001 and batch size 600. ... We set the number of layers to be 3 and the embedding dimension to be 50. ... epoch: 50 for synthetic graphs and 30 for real networks. ... walk length: 5000 for real networks. ... number of eigenvectors k: 40 for synthetic graphs and 200 for real networks. ... batch size: 600 ... learning rate: 0.001 |