Sampling k-partite graphs with a given degree sequence
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Bibliographic record
Abstract
Abstract The authors in the paper [15] presented an algorithm that generates uniformly all the bipartite realizations and the other algorithm that generates uniformly all the simple bipartite realizations whenever A is a bipartite degree sequence of a simple graph. The running time of both algorithms is 𝒪(m),where <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mtext>m</m:mtext> <m:mo>=</m:mo> <m:mfrac> <m:mn>1</m:mn> <m:mn>2</m:mn> </m:mfrac> <m:msubsup> <m:mo>∑</m:mo> <m:mrow> <m:mtext>i</m:mtext> <m:mo>=</m:mo> <m:mn>1</m:mn> </m:mrow> <m:mi>n</m:mi> </m:msubsup> <m:mrow> <m:msub> <m:mrow> <m:mtext>a</m:mtext> </m:mrow> <m:mtext>i</m:mtext> </m:msub> </m:mrow> </m:mrow> </m:math> ${\rm{m}} = {1 \over 2}\sum\nolimits_{\rm {i} = 1}^n {{ \rm{a}_\rm {i}}}$ . Let A =(A 1 : A 2 : ... : A k ) be a k -partite degree sequence of a simple graph, where A i has n i entries such that ∑n i =n. In the present article, we give a generalized algorithm that generates uniformly all the k-partite realizations of A and another algorithm that generates uniformly all the simple k-partite realizations of A. The running time of both algorithms is 𝒪(m), where <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>m</m:mi> <m:mo>=</m:mo> <m:mfrac> <m:mn>1</m:mn> <m:mn>2</m:mn> </m:mfrac> <m:msubsup> <m:mo>∑</m:mo> <m:mrow> <m:mi>i</m:mi> <m:mo>=</m:mo> <m:mn>1</m:mn> </m:mrow> <m:mi>n</m:mi> </m:msubsup> <m:mrow> <m:msub> <m:mrow> <m:mi>a</m:mi> </m:mrow> <m:mi>i</m:mi> </m:msub> </m:mrow> </m:mrow> </m:math> $m = {1 \over 2}\sum\nolimits_{i = 1}^n {{a_i}}$ .
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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