Summary of Graph Convolutional Network For Semi-supervised Node Classification with Subgraph Sketching, by Zibin Huang et al.
Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching
by Zibin Huang, Jun Xian
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Graph-Learning-Dual Graph Convolutional Neural Network (GLDGCN) builds upon the classic Graph Convolutional Neural Network (GCN), introducing dual convolutional layers and graph learning layers. This novel architecture is applied to the semi-supervised node classification task, outperforming baseline methods on three citation networks: Citeseer, Cora, and Pubmed. The paper also explores hyperparameter selection and network depth. Additionally, GLDGCN demonstrates promising results on the KarateClub social network and the Wiki-CS dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new type of computer model called GLDGCN is developed to help computers understand relationships between things. This model works better than others at a specific task called node classification. In this task, computers try to identify what category something belongs to based on its connections to other things. The new model was tested on several different networks of connected things and performed well. It also worked well on two other types of networks. |
Keywords
* Artificial intelligence * Classification * Gcn * Hyperparameter * Neural network * Semi supervised
