Summary of Learning to Model Graph Structural Information on Mlps Via Graph Structure Self-contrasting, by Lirong Wu et al.
Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting
by Lirong Wu, Haitao Lin, Guojiang Zhao, Cheng Tan, Stan Z. Li
First submitted to arxiv on: 9 Sep 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 proposed Graph Structure Self-Contrasting (GSSC) framework, a simple yet effective approach to learn graph structural information without message passing, offers improved robustness and generalization over existing Graph Neural Networks (GNNs). By leveraging Multi-Layer Perceptrons (MLPs), GSSC substitutes explicit message propagation with implicit prior knowledge incorporation. This novel framework consists of two key components: structural sparsification to remove noisy edges and structural self-contrasting in the sparsified neighborhood to learn robust node representations. The bi-level optimization problem is solved in a unified framework, demonstrating encouraging performance and outperforming leading competitors on graph-related tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper proposes a new way to understand graphs without using message passing. It’s like a shortcut that helps machines learn more accurately from graph data. This approach uses simple neural networks instead of complex message-passing algorithms. The framework is divided into two parts: removing unnecessary connections and learning robust node representations. This results in better performance and robustness for handling graph-related tasks. |
Keywords
» Artificial intelligence » Generalization » Optimization
