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..
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
Authors: Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, Vasant Honavar
IJCAI 2019 | Venue PDF | 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 EMAIL |
| 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}. |