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..
Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play
Authors: Wanqi Xue, Youzhi Zhang, Shuxin Li, Xinrun Wang, Bo An, Chai Kiat Yeo
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments in NSGs played on synthetic networks and real-world road networks. Our algorithm significantly outperforms state-of-the-art algorithms in both scalability and solution quality." and "4 Experimental Evaluation We firstly evaluate our algorithm on large-scale NSGs. Then, we perform ablation studies to understand how each component of NSG-NFSP affects the results. |
| Researcher Affiliation | Academia | Wanqi Xue1 , Youzhi Zhang2 , Shuxin Li1 , Xinrun Wang1 , Bo An1 and Chai Kiat Yeo1 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2Department of Computer Science, Dartmouth College, USA |
| Pseudocode | Yes | We provide the overall algorithm in the appendix. |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. |
| Open Datasets | Yes | We evaluate our algorithm in NSGs played on both artificially generated networks and real-world road networks. [...] We extract highways, primary roads and the corresponding intersections from Singapore map via OSMnx [Boeing, 2017]. [...] We generate the evaluation network by the grid model with random edges [Peng et al., 2013]. |
| Dataset Splits | No | The paper mentions training episodes for the DQN attacker (2 * 10^5) and testing episodes (2000), but does not specify a train/validation/test split for a dataset in terms of percentages or counts, or refer to standard predefined splits. |
| Hardware Specification | Yes | Experiments are performed on a server with a 10-core 3.3GHz Intel i9-9820X CPU and an NVIDIA RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions neural networks and deep learning concepts but does not specify any software names with version numbers, such as programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | Neural network structures and hyperparameters are included in the appendix. |