Analysis of Complex Networks: From Biology to Linguistics by Matthias Dehmer, Frank Emmert-Streib

By Matthias Dehmer, Frank Emmert-Streib

Mathematical difficulties resembling graph conception difficulties are of accelerating value for the research of modelling info in biomedical study reminiscent of in platforms biology, neuronal community modelling and so forth. This booklet follows a brand new strategy of together with graph thought from a mathematical point of view with particular purposes of graph concept in biomedical and computational sciences. The e-book is written via well known specialists within the box and gives beneficial heritage info for a large viewers.

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This section explores such a measure, compares it with the symmetry-based measure, and shows its relationship to the graph entropy as defined in [11]. A coloring of a graph is an assignment of colors to the vertices so that no two adjacent vertices have the same color. An n-coloring of a graph G = (V, E) is a coloring with n colors or, more precisely, a mapping f of V onto the set {1, 2, . . , n} such that whenever [u, v] ∈ E, f(u) =/ f(v). The chromatic number κ(G) of a graph G is the smallest value of n for which there is an n-coloring.

However, as soon as more complicated rules for wiring or growing of a network are considered, the seemingly simple concept of a network can become more involved. In particular, in many cases the degree distribution becomes a power law, without any characteristic scale, which raises associations to critical phenomena and scaling phenomena in complex systems. This is the reason why these scale-free types of networks are also called complex networks. A further intriguing aspect of dynamical complex networks is that they can naturally provide some sort of toy model for nonergodic systems, in the sense that not all possible states (configurations) are equally probable or homogeneously populated, and thus can violate a key assumption for systems described by classical statistical mechanics.

Topics in Graph Automorphisms and Reconstruction. Cambridge University Press, Cambridge, 2003. Mowshowitz, A. Entropy and the complexity of graphs: I. An index of the relative complexity of a graph. Bull. Math. Biophys. 30 (1968), pp. 175–204. Mowshowitz, A. Entropy and the complexity of graphs: II. The information content of digraphs and infinite graphs. Bulletin of Mathematical Biophysics 30 (1968), pp. 225–240. Mowshowitz, A. Entropy and the complexity of graphs: III. Graphs with prescribed information content.

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