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Record W1985750603 · doi:10.1145/2641483.2641532

New Properties for Broadcasting in KG2k

2008· article· en· W1985750603 on OpenAlexaff
Sirma Cagil Altay, Hovhannes A. Harutyunyan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsHypercubeVertex (graph theory)Hypercube graphBroadcasting (networking)Computer scienceInterconnectionGraphCombinatoricsMathematicsDiscrete mathematicsTopology (electrical circuits)Theoretical computer scienceComputer networkLine graphVoltage graph

Abstract

fetched live from OpenAlex

Broadcasting is an information dissemination problem described by a group of entities, nodes, connected through an interconnection network. It finds its main application in the field of interconnection networks for parallel architecture. Broadcasting in a graph is the process of transmitting a message from one vertex, the originator, to all other vertices. We will consider the classical model in which an informed vertex informs one of its uninformed neighbours during each time unit. A broadcast graph on n vertices is a graph in which broadcasting can finish in [log2n] time units from any originator. In this paper, we will study new broadcasting properties of Knödel graphs on 2k vertices that we will denote by KG2k. KG2k is known to be a broadcast graph. KG2k may be constructed recursively. Although its different dimensions show some similarities with the dimensions of hypercube topology, KG2k is not edge transitive hence is not as symmetric as hypercube. Moreover, KG2k has the smallest diameter among all known k-regular graphs on 2k vertices. Our study shows new broadcasting properties of KG2k and how to construct new broadcast graphs by deleting some sub-graphs of KG2k. The last property is also characteristic of hypercube.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.130

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.234
Teacher spread0.163 · 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 teacher head, not a consensus.

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

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

Citations0
Published2008
Admission routes1
Has abstractyes

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