Summary of Estimating the History Of a Random Recursive Tree, by Simon Briend et al.
Estimating the history of a random recursive tree
by Simon Briend, Christophe Giraud, Gábor Lugosi, Déborah Sulem
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 The paper investigates the challenge of determining the order in which vertices arrive in a random recursive tree. Two primary models are explored: uniform attachment and linear preferential attachment. A novel order estimation approach is introduced, grounded in Jordan centrality measures, alongside a family of risk metrics to evaluate ordering quality. The authors establish a minimax lower bound for this problem and demonstrate that their proposed estimator nearly achieves optimality. Numerical results indicate that the new method outperforms degree-based and spectral ordering procedures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to figure out the order in which things arrive at a random tree-like structure. There are two main ways that things can attach to the tree: either randomly or based on how big they already are. The researchers come up with a new way to guess the arrival order, using something called Jordan centrality, and test it against other methods. They find that their approach is pretty good and actually beats some simpler methods. |
