Summary of Adaptive Least Mean Pth Power Graph Neural Networks, by Yi Yan et al.
Adaptive Least Mean pth Power Graph Neural Networks
by Yi Yan, Changran Peng, Ercan E. Kuruoglu
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 Adaptive Least Mean pth Power Graph Neural Networks (LMP-GNN) is a universal framework for online prediction of time-varying graph signals in the presence of impulsive noise and missing observations. This approach combines adaptive filtering with graph neural networks, retaining the advantages of both in handling noise and missing data while enabling online updates. The LMP-GNN uses an adaptive update scheme rooted in the minimum dispersion criterion to produce robust estimation results for time-varying graph signals. A special case, the Sign-GNN, is also analyzed. Experimental results on real-world datasets demonstrate the effectiveness and robustness of the proposed LMP-GNN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict changes in networks over time. This method, called LMP-GNN, can handle noisy data and missing information, which is important for many applications like monitoring temperature or traffic patterns. The approach combines two techniques: one that adapts to changing conditions and another that uses neural networks to learn from the data. This combination allows for more accurate predictions and can be used in a variety of situations. |
Keywords
» Artificial intelligence » Gnn » Temperature
