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..
Bandit Online Learning on Graphs via Adaptive Optimization
Authors: Peng Yang, Peilin Zhao, Xin Gao
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark graph datasets show that the proposed bandit algorithm outperforms state-of-the-art competitors, even sometimes beats the algorithms using full information label feedback. |
| Researcher Affiliation | Collaboration | King Abdullah University of Science and Technology, Saudi Arabia 2South China University of Technology, China 3Tencent AI Lab, China |
| Pseudocode | Yes | Algorithm 1 MOLG-F: Adaptive Optimization for Online Learning on Graphs with Full Label Feedback; Algorithm 2 MOLG-B: Adaptive Optimization for Online Learning on Graphs with Bandit Feedback |
| Open Source Code | Yes | Proof. The proof is provided on the website1. 1https://github.com/Young Big Bird1985/MOLG/ |
| Open Datasets | Yes | We exploit 4 real-world graph datasets to evaluate all the algorithms: 1) Coauthor2 is a coauthor graph of the DBLP dataset... 2) Cora2 is a citation graph... 3) IMDB3 is an up-to-date movie dataset... 4) Pub Med4 is a graph... 2http://www.cs.umd.edu/ sen/lbc-proj/data/ 3http://www.imdb.com/ 4http://www.cs.umd.edu/projects/linqs/projects/lbc/ |
| Dataset Splits | No | The paper describes online learning where data is processed sequentially, and models are updated. However, it does not specify explicit train/validation/test splits with percentages or sample counts in the traditional sense for reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers required to replicate the experiments. |
| Experiment Setup | Yes | For both methods, we tune the parameter φ with the grid {10^-2, ..., 10}. For MOLG-B, we fix the exploration parameter ϕt = 0.05 for all t [T]. We set b = 10 for Cora and Coauthor and b = 100 for IMDB and Pub Med due to variable graph structures. Finally, we fix d = 100 for the dimension of low-rank representation. |