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..
Adversarial Learning for Weakly-Supervised Social Network Alignment
Authors: Chaozhuo Li, Senzhang Wang, Yukun Wang, Philip Yu, Yanbo Liang, Yun Liu, Zhoujun Li996-1003
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we evaluate the proposed models over multiple datasets, and the results demonstrate the superiority of our proposals. |
| Researcher Affiliation | Collaboration | 1State Key Lab of Software Development Environment, Beihang University; 2 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics; 3Department of Electrical Computer Engineering, National University of Singapore; 4Tsinghua University; 5Computer Science Department, University of Illinois at Chicago; 6Hortonworks,USA |
| Pseudocode | Yes | Algorithm 1 Training process of SNNAu |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | DBLP: DBLP (http://dblp.uni-trier.de/) is a computer science bibliography website, and its dataset is publicly available |
| Dataset Splits | No | The paper specifies 'training data' and a 'test set' for evaluation but does not explicitly mention a 'validation set' or a split used for validation purposes. |
| Hardware Specification | No | The paper discusses model parameters and training configurations but does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'NTLK stemmer' and 'RMSProp' but does not provide specific version numbers for any of the software dependencies used in the experiments. |
| Experiment Setup | Yes | For our proposals, the dimension d of the latent feature space is set to 100. The discriminator D in all SNNA models is a multi-layer perceptron network with only one hidden layer... The size of minimal training batch is 256, and the learning rate α is set to 0.0001. As mentioned in Algorithm 1, the discriminator will be trained nd times in each training iteration and nd is set to 5. The clipping weight c is 0.01, the annotation weight λc is set to 0.2 and the reconstruction weight λr is set to 0.3. |