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 | Conference PDF | Archive PDF | Plain Text | 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. |