MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
Authors: Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, Vasant Honavar
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results of our experiments on two real-world multi-view data sets show that the embeddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks. |
| Researcher Affiliation | Academia | Yiwei Sun1 , Suhang Wang2 , Tsung-Yu Hsieh1 , Xianfeng Tang2 , Vasant Honavar12 1Department of Computer Science and Engineering, The Pennsylvania State University, USA 2College of Information Sciences and Technology, The Pennsylvania State University, USA {yus162,szw494,tuh45,xut10,vuh14}@psu.edu |
| Pseudocode | Yes | Algorithm 1 MVGAN framework |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is being released. |
| Open Datasets | Yes | We use the following multi-view network data sets[Bui et al., 2016] in our experiments: (i). Last.fm: Last.fm data were collected from the online music network Last.fm1. (ii). Flickr: Flickr data were collected from the Flickr photo sharing service. 1https://www.last.fm |
| Dataset Splits | No | The paper mentions training data and testing data splits but does not specify a separate validation set split or exact percentages for all three phases (e.g., 70/15/15). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 't-SNE package', but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The embedding dimension was set to 128 in all of our experiments. We chose 50% of the nodes randomly for training and the remaining for testing. We used different choices of the dimension d {16, 32, 64, 128, 256, 512}. |