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Record W1902531515 · doi:10.1103/physreve.83.036110

Random sequential renormalization of networks: Application to critical trees

2011· article· en· W1902531515 on OpenAlexaff
Golnoosh Bizhani, Vishal Sood, Maya Paczuski, Peter Grassberger

Bibliographic record

VenuePhysical Review E · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStatistical physicsRenormalizationMathematicsRenormalization groupCritical phenomenaComputer sciencePhysicsMathematical physicsQuantum mechanicsPhase transition

Abstract

fetched live from OpenAlex

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 ${N}_{0}^{\ensuremath{\nu}}\ensuremath{\lesssim}N<{N}_{0}$, where $N$ is the number of nodes at some step in the renormalization and ${N}_{0}$ 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 $\ensuremath{\nu}=1/2$ is derived using random walk and other arguments. The degree distribution becomes broader under successive steps, reaching a power law ${p}_{k}~1/{k}^{\ensuremath{\gamma}}$ with $\ensuremath{\gamma}=2$ and a variance that diverges as ${N}_{0}^{1/2}$ at the end of this regime. Both of these latter results are obtained from a scaling theory. (ii) For ${N}_{0}^{{\ensuremath{\nu}}_{\mathrm{star}}}\ensuremath{\lesssim}N\ensuremath{\lesssim}{N}_{0}^{1/2}$, with ${\ensuremath{\nu}}_{\mathrm{star}}\ensuremath{\approx}1/4$ hubs develop, and fluctuations between different realizations of the RSR are large. Trees are short and fat with an average radius that is $\mathcal{O}(1)$. Crossover functions exhibiting finite-size scaling in the critical region $N~{N}_{0}^{1/2}\ensuremath{\rightarrow}\ensuremath{\infty}$ connect the behaviors in the first two regimes. (iii) For $N\ensuremath{\lesssim}{N}_{0}^{{\ensuremath{\nu}}_{\mathrm{star}}}$, 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.024
GPT teacher head0.332
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2011
Admission routes1
Has abstractyes

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