Summary of Intramix: Intra-class Mixup Generation For Accurate Labels and Neighbors, by Shenghe Zheng et al.
IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors
by Shenghe Zheng, Hongzhi Wang, Xianglong Liu
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel graph neural network (GNN) augmentation method called IntraMix, designed to address two common challenges in graph data: insufficient accurate labels and limited neighbors. Existing methods typically focus on one of these issues or rely on oversimplified strategies, which limits their generalization. IntraMix employs Mixup among inaccurate labeled data of the same class, generating high-quality labeled data at minimal cost. Additionally, it finds high-confidence data to serve as neighbors, enriching the graph topology. The method is theoretically grounded and can be applied to all GNNs. Experimental results demonstrate its effectiveness across various GNNs and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves two big problems in working with graphs. Graphs are complex networks that need special ways of learning from them. Sometimes, the labels we use to teach our models are not very accurate, and sometimes, the nodes (or points) in the graph don’t have many neighbors to learn from. The current methods for solving these problems either only fix one problem or require a lot of extra work. This paper proposes a new method called IntraMix that fixes both problems at once. It takes the inaccurate labels and mixes them with other similar ones, making them better. Then, it finds nodes that are very confident about their group membership and uses those as neighbors for the graph. The results show that this method works well on many different types of graphs. |
Keywords
» Artificial intelligence » Generalization » Gnn » Graph neural network
