Summary of Rsea-mvgnn: Multi-view Graph Neural Network with Reliable Structural Enhancement and Aggregation, by Junyu Chen et al.
RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation
by Junyu Chen, Long Shi, Badong Chen
First submitted to arxiv on: 14 Aug 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 Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation (RSEA-MVGNN) tackles the challenge of combining diverse views in multi-view graph data. Existing approaches prioritize important graph structure features (GSFs) or utilize graph neural networks (GNNs) for feature aggregation, but these methods have limitations. RSEA-MVGNN estimates view-specific uncertainty using subjective logic and designs reliable structural enhancement by feature de-correlation algorithm to achieve diverse feature representation. Additionally, the model evaluates view quality based on opinions learned during training. Experimental results demonstrate that RSEA-MVGNN outperforms state-of-the-art GNN-based methods on five real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RSEA-MVGNN is a new way to combine different views in graph data. Graphs are used to represent complex networks, like social media or phone calls. Right now, we don’t have a good way to join these views together. This makes it hard for computers to learn from the data. The RSEA-MVGNN solves this problem by making sure each view is important and that the computer knows which view is most accurate. This helps the computer learn more accurately. In tests on real-world datasets, RSEA-MVGNN did better than other methods. |
Keywords
» Artificial intelligence » Gnn » Graph neural network
