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..
Efficient Graph Bandit Learning with Side-Observations and Switching Constraints
Authors: Xueping Gong, Jiheng Zhang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments on various types of graphs, including two real-world datasets, demonstrate the efficacy of our proposed methods and their advantages over benchmark methods in graph bandit settings. |
| Researcher Affiliation | Academia | Xueping Gong, Jiheng Zhang The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong S.A.R., China. EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: UCB-GGmax Algorithm 2: LP-GG |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the methodology described is publicly available. |
| Open Datasets | Yes | we conduct numerical experiments on the dataset of roads in a small area of California (Leskovec and Krevl 2014). We conduct numerical experiments on the dataset of products in Amazon (Leskovec and Krevl 2014) Leskovec, J.; and Krevl, A. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/ data. |
| Dataset Splits | No | The paper describes how reward distributions are initialized and graphs are generated for simulations, and mentions the use of real-world datasets, but it does not specify any training/test/validation splits for these datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. It only mentions running simulations. |
| Software Dependencies | No | The paper mentions implementing Q-learning and its hyperparameters but does not specify any software libraries, frameworks, or their version numbers used for the implementation or experiments. |
| Experiment Setup | Yes | Q-learning (Sutton and Barto 2018): we choose the learning rate 0.5, the discount factor 0.9 and the exploration probability min(1, 2|V |/T). |