Learning Strategic Network Emergence Games
Authors: Rakshit Trivedi, Hongyuan Zha
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we demonstrate that MINE learns versatile payoff mechanisms that: highly correlates with the ground truth for a synthetic case; can be used to analyze the observed network structure; and enable effective transfer in specific settings. |
| Researcher Affiliation | Academia | Rakshit Trivedi Georgia Institute of Technology rstrivedi@gatech.edu Hongyuan Zha AIRS and Chinese University of Hong Kong, Shenzhen zha@cc.gatech.edu |
| Pseudocode | Yes | Algorithm 1 in Appendix B outlines complete training procedure. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the methodology described. |
| Open Datasets | Yes | We consider Australian bank dataset [37] (Figure 2(a)), a small network of strategic confiding relationships between branch personnel representing hierarchy among the employees. |
| Dataset Splits | No | The paper mentions splitting data into train and test sets, but does not explicitly describe a separate validation set or its usage for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | No | We provide more details on experimental setup in Appendix C and dataset statistics in Table 2(c). |