Summary of Bangs: Game-theoretic Node Selection For Graph Self-training, by Fangxin Wang et al.
BANGS: Game-Theoretic Node Selection for Graph Self-Training
by Fangxin Wang, Kay Liu, Sourav Medya, Philip S. Yu
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes BANGS, a novel framework for graph self-training that addresses limitations in traditional pseudo-labeling strategies. The approach unifies node selection with conditional mutual information as the objective, providing theoretical guarantees for robustness under noisy objectives. Unlike previous methods, BANGS considers nodes as a collective set in the self-training process, demonstrating superior performance and robustness across various datasets, base models, and hyperparameter settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding new ways to improve graph neural networks (GNNs). GNNs are machine learning models that work with data that has connections between different pieces of information. The researchers came up with a new approach called BANGS that helps GNNs learn better by selecting which parts of the data to focus on. This is important because it can help us make better predictions and decisions from complex data. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning » Self training
