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..
Semi-Implicit Graph Variational Auto-Encoders
Authors: Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments with a variety of graph data show that SIG-VAE significantly outperforms state-of-the-art methods on several different graph analytic tasks. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering, Texas A&M University EMAIL Mc Combs School of Business, The University of Texas at Austin EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation of our proposed model is accessible at https://github.com/sigvae/SIGraph VAE. |
| Open Datasets | Yes | We consider three graph datasets with node attribbutes Citeseer, Cora, and Pubmed [28]. We further consider five graph datasets without node attributes USAir, NS [22], Router [29], Power [34] and Yeast [32]. |
| Dataset Splits | Yes | We preprocess and split the datasets as done in Kipf and Welling [18] with validation and test sets containing 5% and 10% of network links, respectively. |
| Hardware Specification | No | The paper mentions utilizing 'Texas A&M High Performance Research Computing and Texas Advanced Computing Center for providing computational resources' but does not provide specific hardware details such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions 'Tensorflow [1]' and 'The Py GSP package [12]' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We learn the model parameters for 3500 epochs with the learning rate 0.0005 and the validation set used for early stopping. The latent space dimension is set to 16. |