Connectivity in bridge-addable graph classes: the mcdiarmid-steger-welsh conjecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study of typical properties of random graphs is of particular importance for the theoretical analysis of complex networks. In this field, many models of randomness (such as Erdoos-Renyi or random planar graphs, preferential attachment models) have been successfully analysed thanks to the fact that their underlying structure enables one to perform explicit computations of some observables. Another approach, pioneered by McDiarmid, Steger and Welsh (2005) is to consider graphs taken uniformly from an abstract graph class, assuming only some global property of the class but without fully specifying it. Despite the fact that exact computations are no longer possible, results obtained in this setup are arguably very robust, since they apply universally for many different models of random graphs.The foundational and most studied problem in this topic is a conjecture of these authors on bridge-addable classes that we prove in this paper. A class of graphs is bridge-addable if any graph obtained by adding an edge between two connected components of a graph in the class, is also in the class. Examples of bridge-addable classes include forests, planar graphs, graphs with bounded tree-width, or graphs excluding any 2-connected minor. We prove that a random graph from a bridge-addable class is connected with probability at least e-1/2 +o(1), when its number of vertices tends to infinity.This lower bound is tight since it is reached for forests. The best previously known constants where e-1, e-0.7983 and e-2/3 proved respectively by McDiarmid, Steger and Welsh, by Balister, Bollobas and Gerke, and by Norin.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it