Summary of Structure-aware Consensus Network on Graphs with Few Labeled Nodes, by Shuaike Xu et al.
Structure-Aware Consensus Network on Graphs with Few Labeled Nodes
by Shuaike Xu, Xiaolin Zhang, Peng Zhang, Kun Zhan
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a novel framework, Structure-Aware Consensus Network (SACN), for graph node classification with few labeled nodes. SACN leverages a structure-aware consensus learning strategy between two augmented views to effectively utilize unlabeled data and structural information in graphs. The approach integrates graph structures into strong-to-strong consensus learning, achieving better utilization of unlabeled data while maintaining multiview learning. A class-aware pseudolabel selection strategy addresses class imbalance and provides weak-to-strong supervision. SACN outperforms state-of-the-art methods on three benchmark datasets, particularly at very low label rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to classify nodes in graphs when we don’t have many labeled examples. Graphs are like networks of connected things, and node classification is important for tasks like predicting what kind of object is at each point on the graph. The problem with current methods is that they don’t use all the available data or the structural information in the graph. This paper proposes a new approach called SACN (Structure-Aware Consensus Network) that uses a special way to combine labeled and unlabeled data, as well as the structure of the graph. The result is better performance than existing methods on three different datasets. |
Keywords
* Artificial intelligence * Classification
