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Non-negative Matrices and Digraphs

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Encyclopedia of Complexity and Systems Science

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The interplay between Matrix Theory and Graph Theory isfascinating and fruitful, e.?g. [4,5,8,14,18,21,25,31,32]. Here we only deal with theinterplay between nonnegative matrices and digraphs.

Digraphs are of great importance in Computer Science, SocialSciences and Natural Sciences, see [1,2,55]. Nonnegative matrices haveapplications to a variety of disciplines, such as numericalanalysis, probability, game theory, economics, optimization, dynamicalsystems, and data mining, see [3,6,7,39]. These two important subjects,digraphs and nonnegative matrices, are also stronglyconnected. (Nonnegative) matrices are a natural way to describedigraphs and conversely, digraphs describe the zero-nonzerostructure of a matrix. This survey concentrates on the meetingpoints of the two subjects.

The theory of nonnegative matrices is a hundred years old,and digraphs have accompanied it almost since its beginning. Thiscontinues to be true today, when modern applications stimulate...

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Abbreviations

Nonnegative matrix:

nonnegative matrix is a matrix whose entries are realnonnegative numbers. The matrix is positive if all its elements arepositive.

Digraph:

digraph (directed graph) G consists ofa finite set V anda set E of ordered pairsof elements of V. Theelements of V are calledvertices and thoseof E are called arcs. It is often represented graphically bypoints as the vertices of G, and arrows between these pointsas the arcs.

Irreducible matrix:

reducible matrix is a matrix which is either the \( { 1\times 1 }\) zero matrix, or a squarematrix that has a zero submatrix on complementary sets of rowsand columns. A square matrix which is not reducible is irreducible .

Strongly connected digraph:

stronglyconnecteddigraph is a digraph in whichthere is a walk from every vertex to every othervertex.

(Directed) walk:

directed walk (walk) in a digraph isa sequence of arcs of the digraph such that each arc starts atthe end vertex of itspredecessor.

Bibliography

  1. Alon U(2006) An Introduction to Systems Biology: Design Principles ofBiological Circuites. CRC Mathematical and Computational BiologySeries. Chapman, Boca Raton

    Google Scholar 

  2. Bang-JensenJ, Gutin G (2001) Digraphs: Theory Algorithms and Applications.Springer, London

    Google Scholar 

  3. BapatRB, Raghavan TES (1997) Nonnegative Matrices and Applications.Encyclopedia of Mathematics and its Applications 64. CambridgeUniversity Press, Cambridge

    Google Scholar 

  4. BeinekeLW, Wilson RJ (2004) Topics in Algebraic Graph Theory. Encyclopediaof Mathematics and its Applications 102. Cambridge University Press,Cambridge

    Google Scholar 

  5. BermanA (2003) Graphs of matrices and matrices of graphs. Numer Math J ChinUniv (Engl Ser) 12(suppl):12–14

    Google Scholar 

  6. BermanA, Neumann M, Stern RJ (1989) Nonnegative Matrices in DynamicalSystems. Wiley-Interscience, New York

    Google Scholar 

  7. BermanA, Plemmons R (1994) Nonnegative Matrices in the Mathematicalsciences. SIAM, Philadelphia

    MATH  Google Scholar 

  8. BermanA, Shaked-Monderer N (2003) Completely Positive Matrices. WorldScientific, River Edge

    MATH  Google Scholar 

  9. BirkhoffG (1946) Tres obseraciones sobre el algebra lineal. Univ Nac TucumanRev Ser A 5:147–150

    MathSciNet  MATH  Google Scholar 

  10. BondyJA, Murty USR (1976) Graph Theory with Applications. North-Holland,New York

    MATH  Google Scholar 

  11. BrinS, Page L (1998) The anatomy of a large-scale hypertextual Websearch engine. Comput Netw ISDN Syst33(1–7):107–117

    Google Scholar 

  12. BrualdiRA (1979) Matrices permutation equivalent to irreducible matrices andapplications. Linear Multilinear Algebra7:1–12

    MathSciNet  MATH  Google Scholar 

  13. BrualdiRA, Parter SV, Schneider H (1966) The diagonal equivalence ofa nonnegative matrix to a stochastic matrix. J Math AnalAppl 16:31–50

    MathSciNet  MATH  Google Scholar 

  14. BrualdiRA, Ryser HJ (1991) Combinatorial Matrix Theory. Encyclopedia ofMathematics and its Applications 39. Cambridge University Press,Cambridge

    Google Scholar 

  15. BroderA, Kumar R, Maghoul F, Raghavan P, Rajagopalan S, Stata R, Tomkins A,Wiener J (2000) Comput Netw 33(1–3):309–320

    Google Scholar 

  16. ChaikenS (1982) A combinatorial proof of the all-minors matrix treetheorem. SIAM J Alg Dis Meth3:319–329

    MathSciNet  MATH  Google Scholar 

  17. ChebotarevP, Agaev R (2002) Forest matrices around the Laplacian matrix. LinearAlgebra Appl 356:253–274

    MathSciNet  MATH  Google Scholar 

  18. ChungFRK (1997) Spectral Graph Theory. CBMS, Regional Conference Series inMathematics 92. AMS, Providence

    Google Scholar 

  19. ChungF (2005) Laplacians and the Cheeger inequality for directed graphs.Ann Comb 9:1–19

    MathSciNet  MATH  Google Scholar 

  20. ChungF (2006) The diameter and Laplacian eigenvalues of a directedgraphs. Electron J Combin 13(4):6

    Google Scholar 

  21. CvetkovicD, Doob M, Sachs H (1980) Spectra of Graphs: Theory andApplications. Academic Press, New York

    Google Scholar 

  22. EshenbachC, Hall F, Hemansinha R, Kirkland S, Li Z, Shader B, Stuart J, WeaverJ (2000) Properties of Tournaments among Well-Matched Players. AmerMath Month 107(10):881–892

    Google Scholar 

  23. FrobeniusG (1912) Über Matrizen aus nicht negativen Elementen.Sitzungsbericht. Preussische Akademie der Wissenschaften, Berlin,pp 456–477

    Google Scholar 

  24. GantmacherFR (1959) The Theory of Matrices. (Translated from Russian), Chelsea,New York

    Google Scholar 

  25. GodsilC, Royle G (2001) Algebraic Graph Theory. Graduate Texts inMathematics 207. Springer, New York

    MATH  Google Scholar 

  26. GulliA, Signorini A (2005) The indexable web is more than 11.5 billionpages. In: Proc. 14th WWW (Posters),pp 902–903

    Google Scholar 

  27. GuptaRP (1967) On basis digraphs. J Combin Theory 3:309–311

    Google Scholar 

  28. HallP (1935) On representatives of subsets. J London Math Soc10:26–30

    Google Scholar 

  29. HartfielDJ (1970) A simplified form for nearly reducible and nearlydecomposable matrices. Proc Amer Math Soc 24:388–393

    MathSciNet  MATH  Google Scholar 

  30. HershkowitzD (1999) The combinatorial structure of generalizedeigenspaces – from nonnegative matrices to generalmatrices. Linear Algebra Appl302–303:173–191

    MathSciNet  Google Scholar 

  31. HogbenL (2007) Handbook of Linear Algebra. Discrete Mathematics and ItsApplications Series 39. CRC Press, BocaRaton

    Google Scholar 

  32. vander Holst H, Lovász L, Schrijver A (1999) The Colin de Verdièregraph parameter. In: Graph Theory and Computational Biology. BolyaiSoc Math Stud 7:29–85

    Google Scholar 

  33. HornRA, Johnson CR (1985) Matrix Analysis. Cambridge University Press,Cambridge

    MATH  Google Scholar 

  34. KendallMG (1955) Further contributions to the theory of paired comparisons.Biometrics 11:43–62

    MathSciNet  Google Scholar 

  35. KirklandS (2004) Digraph-based conditioning for Markov chains. Linear AlgebraAppl 385:81–93

    MathSciNet  MATH  Google Scholar 

  36. KirklandSJ (2004) A combinatorial approach to the conditioning ofa single entry in the stationary distribution for a Markovchain. Electron J Linear Algebra11:168–179

    MathSciNet  MATH  Google Scholar 

  37. KirklandSJ (2004/5) Girth and subdominant eigenvalues for stochastic matrices.Electron J Linear Algebra 12:25–41

    Google Scholar 

  38. LangvilleAN, Meyer CD (2005) A survey of eigenvector methods of Webinformation retrieval. SIAM Rev47(1):135–161

    MathSciNet  ADS  MATH  Google Scholar 

  39. LeeD, Seung H (2001) Algorithms for nonnegative matrix factorization.Adv Neural Process 13:556–562

    Google Scholar 

  40. MarcusM, Minc H (1963) Disjoint pairs of sets and incidence matrices.Illinois J Math 7:137–147

    MathSciNet  MATH  Google Scholar 

  41. MarcusM, Minc H (1964) A survey of matrix theory and matrixinequalities. Allyn and Bacon, Boston

    MATH  Google Scholar 

  42. MincH (1988) Nonnegative Matrices. Wiley-Interscience Series in DiscreteMathematics and Optimization. Wiley, NewYork

    Google Scholar 

  43. PerronO (1907) Zur Theorie der Matrizen. Math Ann 64:248–263

    MathSciNet  MATH  Google Scholar 

  44. RichmanD, Schneider H (1978) On the singular graph and the Weyrcharacteristic of an M-matrix.Aequationes Math 17:208–234

    MathSciNet  MATH  Google Scholar 

  45. RomanovskyV (1936) Recherche sur les chains de Markoff. Acta Math66:147–251

    MathSciNet  Google Scholar 

  46. RothblumUG (1975) Algebraic eigenspaces of nonnegative matrices. LinearAlgebra Appl 12:281–292

    MathSciNet  MATH  Google Scholar 

  47. SchneiderH (1956) The elementary divisors associated with 0 of a singularM-matrix. Proc Edinburgh Math Soc10(2):108–122

    MathSciNet  Google Scholar 

  48. SchneiderH (1977) The concepts of irreducibility and full indecomposability ofa matrix in the works of Frobenius, König and Markov. LinearAlgebra Appl 18:139–162

    MATH  Google Scholar 

  49. SchneiderH (1986) The influence of the marked reduced graph ofa nonnegative matrix on the Jordan Form and on relatedproperties: a survey. Linear Algebra Appl84:161–189

    MathSciNet  MATH  Google Scholar 

  50. SenetaE (1981) Nonnegative Matrices and Markov Chains. Springer Series inStatistics, 2nd edn. Springer, New York

    Google Scholar 

  51. TamBS (2004) The Perron generalized eigenspace and the spectral cone ofa cone-preserving map. Linear Algebra Appl393:375–429

    MathSciNet  MATH  Google Scholar 

  52. TutteWT (1948) The dissection of equilateral triangles into equilateraltriangles. Proc Cambridge Philos Soc7:463–482

    MathSciNet  ADS  Google Scholar 

  53. TutteWT (1980) Graph Theory. Encyclopedia of Mathematics and itsApplications 21. Cambridge University Press,Cambridge

    Google Scholar 

  54. VictoryJr HD (1985) On nonnegative solutions to matrix equations. SIAM J AlgDis Meth 6:406–412

    MathSciNet  MATH  Google Scholar 

  55. WassermanS, Faust K (1994) Social network analysis: Methods and applications.Cambridge University Press, Cambridge

    Google Scholar 

  56. WeiTH (1952) The Algebraic Foundations of Ranking Theory. Ph?D Thesis,Cambridge University

    Google Scholar 

  57. WuCW (2005) On Rayleigh-Ritz rations of a generalized Laplacianmatrix of directed graphs. Linear Algebra Appl402:207–227

    MathSciNet  MATH  Google Scholar 

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Berman, A., Shaked-Monderer, N. (2009). Non-negative Matrices and Digraphs. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_368

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