Summary of Overcoming Class Imbalance: Unified Gnn Learning with Structural and Semantic Connectivity Representations, by Abdullah Alchihabi et al.
Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations
by Abdullah Alchihabi, Hao Yan, Yuhong Guo
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 addresses the issue of class imbalance in real-world graph datasets, where a small number of annotated nodes belong to a majority class and many other classes have only a few labeled nodes. Graph Neural Networks (GNNs) struggle to generalize effectively on minority classes due to overfitting and bias towards majority classes. The proposed Unified Graph Neural Network Learning (Uni-GNN) framework integrates structural and semantic connectivity representations through node encoders, enabling efficient diffusion of discriminative information throughout the graph. Additionally, a balanced pseudo-label generation mechanism is employed to augment the pool of available labeled nodes from minority classes in the training set. Experimental results show that Uni-GNN outperforms state-of-the-art class-imbalanced graph learning baselines across multiple benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers tackle a common problem with machine learning models when working with graphs: class imbalance. This means that most of the labeled nodes belong to one or two classes, while many other classes have very few labeled nodes. They show that current methods, called Graph Neural Networks (GNNs), don’t do well on these imbalanced datasets because they get biased towards the majority classes and can’t generalize well to minority classes. To fix this, they introduce a new way of processing graph data called Uni-GNN. This method combines information from different parts of the graph to make better predictions. They also use some clever tricks to create more labeled data for minority classes, which helps improve performance. |
Keywords
» Artificial intelligence » Diffusion » Gnn » Graph neural network » Machine learning » Overfitting
