Semi-Implicit Graph Variational Auto-Encoders
Authors: Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 {armanihm, ehsanr, duffieldng, krn, xqian}@tamu.edu Mc Combs School of Business, The University of Texas at Austin mingyuan.zhou@mccombs.utexas.edu |
| 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. |