Summary of Graffin: Stand For Tails in Imbalanced Node Classification, by Xiaorui Qi et al.
Graffin: Stand for Tails in Imbalanced Node Classification
by Xiaorui Qi, Yanlong Wen, Xiaojie Yuan
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 A novel approach, Graffin, is proposed to address the issue of imbalanced graph representation learning (GRL) models. Traditional GRL models assume a balanced distribution of input graphs, which is not realistic in real-world scenarios. This imbalance can lead to poor model performance on tail data. To alleviate this issue, Graffin incorporates tail data augmentation, inspired by recurrent neural networks (RNNs). The module flows head features into tail data through graph serialization techniques, fusing local and global structures to enrich the semantics of tail data. Evaluations on four real-world datasets demonstrate that Graffin can improve adaptation to tail data without significantly degrading overall model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graffin is a new way to help machine learning models learn from graphs. Graphs are like networks where nodes connect to each other. In the real world, these graphs often have imbalanced information, which means some parts have much more information than others. This makes it hard for machines to learn from them. Graffin is designed to fix this problem by taking information from the part with plenty of data and sharing it with the part that has less data. It does this by looking at how nodes are connected in the graph, which helps the model understand what’s important. By doing this, Graffin can help machine learning models learn better from graphs. |
Keywords
» Artificial intelligence » Data augmentation » Machine learning » Representation learning » Semantics
