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 [1].
Mixed Strategies for Security Games with General Defending Requirements
Authors: Rufan Bai, Haoxing Lin, Xinyu Yang, Xiaowei Wu, Minming Li, Weijia Jia
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate the effectiveness of our algorithm in several large real-world datasets. |
| Researcher Affiliation | Academia | Rufan Bai1 , Haoxing Lin2 , Xinyu Yang1 , Xiaowei Wu1 , Minming Li3 , Weijia Jia4 5 1 Io TSC, University of Macau 2 National University of Singapore 3 City University of Hong Kong 4 BNU-UIC 5 Beijing Normal University (Zhuhai) |
| Pseudocode | Yes | Algorithm 1 Patching, Algorithm 2 Find R |
| Open Source Code | No | The paper does not include an unambiguous statement or a direct link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | All the datasets are downloaded from SNAP by Stanford [Leskovec and Krevl, 2014]. |
| Dataset Splits | No | The paper describes the datasets used and how parameters for instances are set, but it does not provide specific details on train/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components or libraries used (e.g., Python, PyTorch, or solvers). |
| Experiment Setup | No | The paper specifies parameters for generating dataset instances (e.g., values for αu, Ξu, wuv, and total resource R), but it does not provide explicit details on experimental setup for the algorithm's execution, such as hyperparameters (learning rate, batch size, epochs, optimizer settings) or system-level training settings. |