Summary of Neural Symbolic Regression Of Complex Network Dynamics, by Haiquan Qiu et al.
Neural Symbolic Regression of Complex Network Dynamics
by Haiquan Qiu, Shuzhi Liu, Quanming Yao
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Symbolic Computation (cs.SC)
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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 proposed Physically Inspired Neural Dynamics Symbolic Regression (PI-NDSR) method learns symbolic expressions of complex network dynamics using neural networks and genetic programming. This approach, comprising a Physically Inspired Neural Dynamics (PIND) component for augmenting and denoising trajectories and a coordinated genetic search algorithm, leverages references from node and edge dynamics to derive symbolic expressions. The PI-NDSR is evaluated on synthetic and real-world datasets, including disease spreading, and demonstrates improved performance over existing methods in terms of recovery probability and error. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand complex networks that appear in nature and society. It’s all about how these networks change over time. But figuring out how they change can be really hard and requires specialized knowledge. Researchers have developed a new method called PI-NDSR to make this process easier. This method uses artificial intelligence and genetics to automatically learn the rules that govern how complex networks change. The researchers tested their method on some fake data and real-world data about disease spreading, and it worked better than other methods. |
Keywords
» Artificial intelligence » Probability » Regression
