Networks, markov lie monoids, and generalized entropy

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Abstract

The continuous general linear group in n dimensions can be decomposed into two Lie groups: (1) an n(n-1) dimensional 'Markov type' Lie group that is defined by preserving the sum of the components of a vector, and (2) the n dimensional Abelian Lie group, A(n), of scaling transformations of the coordinates. With the restriction of the first Lie algebra parameters to nonnegative values, one obtains exactly all Markov transformations in n dimensions that are continuously connected to the identity. In this work we show that every network, as defined by its C matrix, is in one to one correspondence to one element of the Markov monoid of the same dimensionality. It follows that any network matrix, C, is the generator of a continuous Markov transformation that can be interpreted as producing an irreversible flow among the nodes of the corresponding network. © Springer-Verlag Berlin Heidelberg 2005.

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Johnson, J. E. (2005). Networks, markov lie monoids, and generalized entropy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3685 LNCS, pp. 129–135). Springer Verlag. https://doi.org/10.1007/11560326_10

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