Random sequential renormalization of networks: Application to critical trees
Bibliographic record
Abstract
We introduce the concept of random sequential renormalization (RSR) for arbitrary networks. RSR is a graph renormalization procedure that locally aggregates nodes to produce a coarse grained network. It is analogous to the (quasi)parallel renormalization schemes introduced by C. Song et al. [C. Song et al., Nature (London) 433, 392 (2005)] and studied by F. Radicchi et al. [F. Radicchi et al., Phys. Rev. Lett. 101, 148701 (2008)], but much simpler and easier to implement. Here we apply RSR to critical trees and derive analytical results consistent with numerical simulations. Critical trees exhibit three regimes in their evolution under RSR. (i) For N₀{ν}≲N<N₀, where N is the number of nodes at some step in the renormalization and N₀ is the initial size of the tree, RSR is described by a mean-field theory, and fluctuations from one realization to another are small. The exponent ν=1/2 is derived using random walk and other arguments. The degree distribution becomes broader under successive steps, reaching a power law p{k}~1/k{γ} with γ=2 and a variance that diverges as N₀¹/² at the end of this regime. Both of these latter results are obtained from a scaling theory. (ii) For N₀{ν{star}}≲N ≲ N₀¹/², with ν_{star}≈1/4 hubs develop, and fluctuations between different realizations of the RSR are large. Trees are short and fat with an average radius that is O(1). Crossover functions exhibiting finite-size scaling in the critical region N~N₀¹/²→∞ connect the behaviors in the first two regimes. (iii) For N ≲ N₀{ν{star}}, star configurations appear with a central hub surrounded by many leaves. The distribution of stars is broadly distributed over this range. The scaling behaviors found under RSR are identified with a continuous transition in a process called "agglomerative percolation" (AP), with the coarse-grained nodes in RSR corresponding to clusters in AP that grow by simultaneously attaching to all their neighboring clusters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".