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..
Effective Decoding in Graph Auto-Encoder Using Triadic Closure
Authors: Han Shi, Haozheng Fan, James T. Kwok906-913
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on link prediction, node clustering and graph generation show that the use of triads leads to more accurate prediction, clustering and better preservation of the graph characteristics. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Engineering Hong Kong University of Science and Technology, Hong Kong 2Amazon |
| Pseudocode | Yes | Algorithm 1 Training the triad variational graph autoencoder (TVGA) and triad graph auto-encoder (TGA) using SGD. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of its source code. |
| Open Datasets | Yes | Experiments are performed on three standard benchmark citation graph data sets1 (Sen et al. 2008): Cora, Citeseer, and Pubmed (Table 1). 1http://www.cs.umd.edu/ sen/lbc-proj/LBC.html |
| Dataset Splits | Yes | In this experiment, 85% of the edges and non-edges (unconnected nodes) from each graph are randomly selected to form the training set, another 10% is used as the validation set, and the remaining 5% as testing set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam (Kingma and Ba 2014) is the optimizer' but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The proposed algorithm uses a mini-batch size of 5,000. Adam (Kingma and Ba 2014) is the optimizer, with a learning rate of 0.0005. Both the hidden layer and embedding layer of the encoder have 32 hidden units. The convolution layer in the triad decoder has 4 ο¬lters (i.e., the dimension of ztriplet is 1 32 4). |