Summary of Universal Inceptive Gnns by Eliminating the Smoothness-generalization Dilemma, By Ming Gu et al.
Universal Inceptive GNNs by Eliminating the Smoothness-generalization Dilemma
by Ming Gu, Zhuonan Zheng, Sheng Zhou, Meihan Liu, Jiawei Chen, Tanyu Qiao, Liangcheng Li, Jiajun Bu
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 proposes a unified framework for Graph Neural Networks (GNNs) to handle varying levels of homophily in different graph orders. It identifies the cascade dependency as an overlooked architectural aspect affecting learning processes, especially in high-order neighborhoods and heterophilic graphs. The authors introduce Inceptive Graph Neural Network (IGNN), a message-passing framework that replaces the cascade dependency with an inceptive architecture, providing personalized generalization capabilities and capturing neighborhood-wise relationships to select receptive fields. IGNN outperforms 23 baseline methods on both homophilic and heterophilic graphs while scaling efficiently to large graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in Graph Neural Networks (GNNs) by creating a new way for GNNs to work with different types of relationships between nodes. This is important because many real-world networks have different types of connections between nodes, making it hard for GNNs to learn from these networks. The authors design a new type of GNN called Inceptive Graph Neural Network (IGNN) that can handle these different types of relationships and learn from them better than other methods. |
Keywords
» Artificial intelligence » Generalization » Gnn » Graph neural network
