Graphite: Iterative Generative Modeling of Graphs
Authors: Aditya Grover, Aaron Zweig, Stefano Ermon
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a wide variety of synthetic and benchmark datasets, Graphite outperforms competing approaches for the tasks of density estimation, link prediction, and node classification. We evaluate Graphite on tasks involving entire graphs, nodes, and edges. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University, USA. |
| Pseudocode | No | The paper describes algorithmic steps and equations, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, such as a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | Datasets. We compared across standard benchmark citation network datasets: Cora, Citeseer, and Pubmed with papers as nodes and citations as edges (Sen et al., 2008). |
| Dataset Splits | Yes | For each family, 300 graph instances were sampled with each instance having 10−20 nodes and evenly split into train/validation/test instances. In our experiments, we held out a set of 5% edges for validation, 10% edges for testing, and train all models on the remaining subgraph. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments, only mentioning general support from |
| Software Dependencies | No | The paper mentions general tools like |
| Experiment Setup | No | Additional hyperparameter details are described in Appendix B. |