Summary of Mixture Of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification, by Xuanze Chen et al.
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
by Xuanze Chen, Jiajun Zhou, Shanqing Yu, Qi Xuan
First submitted to arxiv on: 11 Dec 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 GNNMoE is a universal model architecture for node classification that addresses limitations in current graph neural networks (GNNs). GNNs excel at representation learning but struggle with heterophilous data and long-range dependencies. Graph transformers, which use self-attention, face scalability and noise challenges on large-scale graphs. GNNMoE combines fine-grained message-passing operations with a mixture-of-experts mechanism to build feature encoding blocks. It also incorporates soft and hard gating layers to assign the most suitable expert networks to each node. Additionally, adaptive residual connections and an enhanced FFN module improve expressiveness of node representation. Experimental results demonstrate GNNMoE’s performance across various graph types, alleviating over-smoothing and global noise while ensuring computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNMoE is a new way for computers to understand complex patterns in networks like social media or traffic routes. Current computer models are good at learning about small parts of these networks but struggle when they’re big and complicated. GNNMoE fixes this by combining different approaches to find the best way to learn about each part of the network. It’s really good at finding patterns that other models might miss, and it works well on big networks too! |
Keywords
» Artificial intelligence » Classification » Mixture of experts » Representation learning » Self attention
