Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Neural Link Prediction over Aligned Networks
Authors: Xuezhi Cao, Haokun Chen, Xuejian Wang, Weinan Zhang, Yong Yu
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,210 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,834 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that MNN outperforms the state-of-the-art methods and achieves 3% to 5% relative improvement of AUC score across different settings, particularly over 8% for cold start scenarios. |
| Researcher Affiliation | Academia | Xuezhi Cao, Haokun Chen, Xuejian Wang, Weinan Zhang, Yong Yu APEX Data & Knowledge Management Lab Shanghai Jiao Tong University cxz,chenhaokun,xjwang,wnzhang,EMAIL |
| Pseudocode | No | The paper describes the network design with mathematical equations and text, but it does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | The source code as well as the datasets are available online1. 1http://apex.sjtu.edu.cn/projects/34 |
| Open Datasets | Yes | We conduct experiments using two sets of aligned social networks, provided by (Cao and Yu 2016a). |
| Dataset Splits | No | The paper mentions '80% links are used for training' but does not specify a validation split or percentage. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers (e.g., Python 3.x, TensorFlow 2.x). |
| Experiment Setup | Yes | For our multi-neural-network model (MNN), we set the embedding dimension k = 80, sampling rate α = 100, weighting parameter β = 0.5 and the regularization term γ = 0.1. We design each neural network to have 2 hidden layers between the product layer and output layer, with width of 100 and 50 respectively. |