GraphGAN: Graph Representation Learning With Generative Adversarial Nets
Authors: Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on real-world datasets, we demonstrate that Graph GAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | 1Shanghai Jiao Tong University, wanghongwei55@gmail.com, {wnzhang, myguo}@sjtu.edu.cn 2Microsoft Research Asia, {fuzzhang, xing.xie}@microsoft.com 3The Hong Kong Polytechnic University, {csjiawang, csmiaozhao}@comp.polyu.edu.hk 4Huazhong University of Science and Technology, wangjialin@hust.edu.cn |
| Pseudocode | Yes | Algorithm 1 Online generating strategy for the generator. Algorithm 2 Graph GAN framework. |
| Open Source Code | Yes | 1https://github.com/hwwang55/Graph GAN |
| Open Datasets | Yes | We utilize the following five datasets in our experiments: ar Xiv-Astro Ph2 is from the e-print ar Xiv... ar Xiv-Gr Qc3 is also from ar Xiv... Blog Catalog4... Wikipedia5... Movie Lens-1M6... 2https://snap.stanford.edu/data/ca-Astro Ph.html 3https://snap.stanford.edu/data/ca-Gr Qc.html 4http://socialcomputing.asu.edu/datasets/Blog Catalog 5http://www.mattmahoney.net/dc/textdata 6https://grouplens.org/datasets/movielens/1m/ |
| Dataset Splits | Yes | The above hyper-parameters are chosen by cross validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. It only mentions performing 'stochastic gradient descent'. |
| Software Dependencies | No | The paper mentions using 'logistic regression method' and refers to 'Skip-Gram' as a component of baselines, but it does not specify software dependencies with version numbers (e.g., Python 3.x, TensorFlow 2.x, scikit-learn 0.x). |
| Experiment Setup | Yes | For all three experiment scenarios, we perform stochastic gradient descent to update parameters in Graph GAN with learning rate 0.001. In each iteration, we set s as 20 and t as the number of positive samples in the test set for each vertex, then run G-steps and D-steps for 30 times, respectively. The dimension of representation vectors k for all methods is set as 20. |