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..
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Authors: Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, Jure Leskovec
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that Graph RNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 larger than previous deep models. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University, Stanford, CA, 94305 2Department of Computer Science, University of Southern California, Los Angeles, CA, 90007. |
| Pseudocode | Yes | Algorithm 1 Graph RNN inference algorithm |
| Open Source Code | Yes | 1The code is available in https://github.com/ snap-stanford/Graph RNN |
| Open Datasets | Yes | Community. 500 two-community graphs...generated by the Erd os-R enyi model (E-R) (Erd os & R enyi, 1959)...Protein. 918 protein graphs (Dobson & Doig, 2003)...Ego. 757 3-hop ego networks extracted from the Citeseer network (Sen et al., 2008) |
| Dataset Splits | No | We use 80% of the graphs in each dataset for training and test on the rest. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing the model but does not specify any software dependencies with version numbers (e.g., specific deep learning frameworks like PyTorch or TensorFlow versions). |
| Experiment Setup | No | The hyperparameter settings for Graph RNN were fixed after development tests on data that was not used in follow-up evaluations (further details in the Appendix). |