UGC: Universal Graph Coarsening
Authors: Mohit Kataria, Sandeep Kumar, Jayadeva Dr
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on benchmark datasets demonstrate that UGC preserves spectral similarity while coarsening. In comparison to existing methods, UGC is 4 to 15 faster, has lower eigen-error, and yields superior performance on downstream processing tasks even at 70% coarsening ratios. |
| Researcher Affiliation | Academia | 1 Yardi School of Artificial Intelligence 2Department of Electrical Engineering 3Bharti School of Telecommunication Technology and Management Indian Institute of Technology Delhi |
| Pseudocode | Yes | The pseudocode for UGC is listed in Algorithm 1. |
| Open Source Code | No | The paper states "Code is available at UGC" in a footnote, but does not provide a direct, unambiguous link or specific instructions for access within the paper itself to satisfy the "concrete access" requirement. |
| Open Datasets | Yes | Our experiments cover widely adopted benchmarks, including Cora ,Citeseer, Pubmed [36], CS, Physics [37], DBLP [38]. Additionally, UGC effectively coarsens large datasets like Flickr, Yelp [39], and Reddit [40], previously challenging for existing techniques. We also present datasets like Squirrel, Chameleon, Texas, Film, Wisconsin [11, 12, 16, 17], characterized by dominant heterophilic factors. |
| Dataset Splits | Yes | We randomly split data in 60%, 20%, 20% for the training-validation-test. |
| Hardware Specification | Yes | All the experiments conducted for this work were performed on an Intel Xeon W-295 CPU and 64GB of RAM desktop using the Python environment. |
| Software Dependencies | No | The paper mentions using 'Python environment' and lists GNN models (GCN, Graph Sage, GIN, GAT) but does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | We employed a single hidden layer GCN model with standard hyperparameters values [13] see Appendix H for the node-classification task. Table 8: GNN model parameters. MODEL HIDDEN LAYERS LEARNING RATE DECAY EPOCH GCN {64, 64} 0.003 0.0005 500 GRAPHSAGE {64, 64} 0.003 0.0005 500 GIN {64, 64} 0.003 0.0005 500 GAT {64, 64} 0.003 0.0005 500 |