Summary of Grassnet: State Space Model Meets Graph Neural Network, by Gongpei Zhao et al.
GrassNet: State Space Model Meets Graph Neural Network
by Gongpei Zhao, Tao Wang, Yi Jin, Congyan Lang, Yidong Li, Haibin Ling
First submitted to arxiv on: 16 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 This paper proposes a novel graph neural network architecture called Graph State Space Network (GrassNet), which addresses limitations in traditional spectral graph neural networks (GNNs). Specifically, existing polynomial-based methods encounter issues due to low-order truncation and lack of overall modeling of the graph spectrum. GrassNet employs structured state space models (SSMs) to capture correlations between graph signals at different frequencies and derives a unique rectification for each frequency in the graph spectrum. This approach theoretically offers greater expressive power compared to polynomial filters. The authors demonstrate the effectiveness of GrassNet on nine public benchmarks, achieving superior performance in real-world graph modeling tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating new ways to analyze and understand complex networks, like social media or transportation systems. Right now, we have some tools that can do this job, but they’re not very good at handling certain types of data. The researchers propose a new approach called GrassNet, which uses a special kind of mathematical model to better understand the relationships between different parts of these networks. This new method is more powerful than what we had before and can be used to solve real-world problems. |
Keywords
» Artificial intelligence » Graph neural network
