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Record W2592318313 · doi:10.21914/anziamj.v58i0.10993

Isolated Scattering Number Can be Computed in Polynomial Time for Interval Graphs

2017· article· en· W2592318313 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueANZIAM Journal · 2017
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Graph Theory Research
Canadian institutionsnot available
FundersChina Scholarship CouncilNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsInterval graphPlanarity testingCombinatoricsMathematicsDiscrete mathematicsInterval (graph theory)ConjectureGraph theoryGraphIndifference graphChordal graph1-planar graph

Abstract

fetched live from OpenAlex

The isolated scattering number of an incomplete connected graph\(~G\) is defined as \(\operatorname{isc}(G)=\max\{i(G-X)-|X|:X\in C(G)\}\), where\(~i(G-X)\) and\(~C(G)\), respectively, denote the number of components which are isolated vertices and the set of all separators of\(~G\). The isolated scattering number is a comparatively better parameter to measure the vulnerability of networks. We give a polynomial time algorithm to compute the isolated scattering number of interval graphs, a subclass of co-comparability graphs. Our result can also be used to compute isolated scattering number of proper interval graph. References C. A. Barefoot, R. Entringer and H. Swart. Vulnerability in graphs–-A comparative survey. J. Combin. Math. Combin. Comput. 1:12–22, 1987. https://www.researchgate.net/publication/266002676 J. A. Bondy and U. S. R. Murty. Graph Theory with Applications. Macmillan, London and Elsevier, New york, 1976. http://101.96.10.59/www.iro.umontreal.ca/ hahn/IFT3545/GTWA.pdf K. S. Booth and G. S. Lueker. Testing for the consecutive ones property, interval graphs, and graph planarity using PQ-tree algorithms. J. Comput. System Sci. 13(3):335–379, 1976. doi:10.1016/S0022-0000(76)80045-1 H. Broersma, J. Fiala, P. Golovach, T. Kaiser, D. Paulusma, A. Proskurowski. Linear-time algorithms for scattering number and hamilton-connectivity of interval graphs. J Graph Theory 79(4): 282-299, 2015. doi:10.1002/jgt.21832 M. C. Carlisle, E. L. Loyd. On the k-coloring of intervals. LNCS 497: 90–101, 1991. doi:10.1016/0166-218X(95)80003-M V. Chvatal. Tough graphs and Hamiltonian circuits. Discrete Mathematics 5:215–228, 1973. doi:10.1016/j.disc.2006.03.011 M. Cozzens, D. Moazzami and S. Stueckle. The tenacity of a graph. Proc. 7th International Conference on the Theory and Applications of Graphs, Wiley, New York, 1111–1122, 1995. http://101.96.10.59/www.iro.umontreal.ca/ hahn/IFT3545/GTWA.pdf J. Fabri. Automatic Storage Optimization. UMI Press Ann Arbor, MI, 1982. doi:10.1145/989393.989398 P. C. Gilmore and A. J. Hoffman. A characterization of comparability graphs and of interval graphs. Canadian J. Math. 16(99):539–548, 1964. doi:10.1142/97898127969360006 M. C. Golumbic. Algorithmic Graph Theory and Perfect Graphs. Academic Press, 1980. doi:10.1007/BF00390110 H. A. Jung. On maximal circuits in finite graphs. Ann Discrete Math. 3:129–144, 1978. doi:10.1016/S0167-5060(08)70503-X J. R. Jungck, O. Dick, and A. G. Dick. Computer assisted sequencing, interval graphs and molecular evolution. Biosystem 15:259–273, 1982. doi:10.1016/0303-2647(82)90010-7 T. Kloks and D. Kratschz. Listing all minimal separators of a graph. SIAM J. Comput. 27(3):605–613, 1998. doi:10.1137/S009753979427087X T. Kloks, D. Kratsch and J. Spinrad. Tree-width and path-width of co-comparability graphs of bounded dimension. Computing Science Note. Eindhoven University of Technology, Eindhoven, The Netherlands. 93-46. https:alexandria.tue.nl/extra1/wskrap/publichtml/9313455.pdf D. Kratsch, T. Klocks and H. Muller. Computing the toughness and the scattering number for interval and other graphs. IRISA resarch report. France, 1994. https://www.researchgate.net/publication/2646060 F. W. Li. On isolated rupture degree of graphs. Utilitas Mathematica 96: 33–47, 2015. https://www.researchgate.net/publication/292526797 F. W. Li. Isolated rupture degree of trees and gear graphs. Neural Network World 25(3): 287–300, 2015. doi:10.14311/NNW.2015.25.015 F. W. Li and X. L. Li. Neighbor-scattering number can be computed in polynomial time for interval graphs. Computers and Mathematics with Applications 54(5):679–686, 2007. doi:10.1016/j.camwa.2007.02.006 Y. K. Li, S. G. Zhang and X. L. Li. Rupture degree of graphs. Int. J. Comput. Math. 82(7):793–803, 2005. doi:10.1080/00207160412331336062 T. Ohtsuki, H. Mori, Khu. E. S., T. Kashiwabara, T. Fujisawa. One dimensional logic gate assignment and interval graph. IEEE Trans. Circuits and Systems 26:675–684, 1979. doi:10.1109/TCS.1979.1084695 S. Y. Wang, Y. X. Yang, S. W. Lin, J. Li and Z. M. Hu. The isolated scattering number of graphs. Acta Math. Sinica (in Chinese) 54(5):861–874, 2011. http://en.cnki.com.cn/Article_en/CJFDTotal-SXXB201105015.htm

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 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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.030
GPT teacher head0.326
Teacher spread0.296 · 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