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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
UGC: Universal Graph Coarsening
Authors: Mohit Kataria, Sandeep Kumar, Jayadeva Dr
NeurIPS 2024 | Venue PDF | 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 |