Summary of Learning Networks From Wide-sense Stationary Stochastic Processes, by Anirudh Rayas et al.
Learning Networks from Wide-Sense Stationary Stochastic Processes
by Anirudh Rayas, Jiajun Cheng, Rajasekhar Anguluri, Deepjyoti Deka, Gautam Dasarathy
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 A novel approach is proposed to learn the edge connectivity of complex networked systems driven by latent inputs, common in fields like neuroscience, finance, and engineering. The key inference problem is to infer edge connectivity from node outputs (potentials). The paper focuses on systems governed by steady-state linear conservation laws, where the Laplacian matrix encodes the edge structure. Given temporally correlated samples of node potentials, the authors learn the support of the Laplacian using an _1-regularized Whittle’s maximum likelihood estimator (MLE). This method is particularly useful for learning large-scale networks in high-dimensional settings where the network size significantly exceeds the number of samples. The proposed approach has applications in fields like neuroscience and finance, where understanding complex networked systems is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand how different parts of a complex system work together. In many fields, like biology or economics, we have data about how these parts behave. But it’s hard to figure out which parts are connected and how they affect each other. Researchers developed a new way to solve this problem by looking at patterns in the data. They tested their method on large networks with many parts and found that it worked well. This could help us better understand complex systems like the brain or financial markets. |
Keywords
» Artificial intelligence » Inference » Likelihood
