Summary of Infinite-horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation, by Ruizhe Zhang et al.
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation
by Ruizhe Zhang, Xinke Jiang, Yuchen Fang, Jiayuan Luo, Yongxin Xu, Yichen Zhu, Xu Chu, Junfeng Zhao, Yasha Wang
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper proposes a new Graph Neural Network (GNN) architecture called the Graph Power Filter Neural Network (GPFN), which enhances node classification by employing a power series graph filter to augment the receptive field. The GPFN designs a new way to build a graph filter with an infinite receptive field based on the convergence power series, allowing it to capture long-range dependencies. Theoretical analysis proves that the GPFN is a general framework that can integrate any power series and outperform state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of Graph Neural Network (GNN) called GPFN, which helps make decisions better by looking at more information from a graph. It uses something called a “power series” to do this, which is like a formula that never ends. This new way of building a GPFN lets it look at really long distances in the graph and makes it better than other ways of doing things. |
Keywords
* Artificial intelligence * Classification * Gnn * Graph neural network * Neural network
