Matrix tree theorem
Let be a graph with vertices and edges , some of which may be oriented. The incidence matrix of is the -matrix whose entries are given by if is a non-oriented link (i.e. an edge that is not a loop) incident to or if is an oriented link with head , if is an oriented link with tail , if is a loop (necessarily non-oriented) at , and otherwise. The mixed Laplacian matrix of is defined as . It is easy to see that the diagonal entries of give the degrees of the vertices with, however, each loop contributing to the count, and the off-diagonal entry gives the number of non-oriented edges joining and minus the number of oriented edges joining them. Let denote the number of spanning trees of , with orientation ignored.
The matrix tree theorem in its classical form, which is already implicit in the work of G. Kirchhoff [a9], states that if is the Laplacian of any orientation of a loopless undirected graph and is the matrix obtained by deleting any row and column of , then ; that is, each cofactor of is equal to the tree-number of . If denotes the adjoint of the matrix and denotes the matrix with all entries equal to , then . The proof of this theorem uses the Binet–Cauchy theorem to expand the cofactor of together with the fact that every non-singular -minor of (cf. also Minor) comes from a spanning tree of having value . In the case of the complete graph (with some orientation), , and it can be seen that , which is Cayley's formula for the number of labelled trees on vertices [a4]. Temperley's result [a3], Prop. 6.4, avoids using the cofactor notation in the following form: . It is interesting to note that this determinantal way of computing requires operations rather than the operations when using recursion [a17], p. 66.
For a loopless directed graph , let and , where and are the diagonal matrices of in-degrees and out-degrees in , and the -entry of is the number of edges from to . An out-tree is an orientation of a tree having a root of in-degree and all other vertices of in-degree . An in-tree is an out-tree with its edges reversed. W.T. Tutte [a16] extended the matrix tree theorem by showing that the number of out-trees (respectively, in-trees) rooted at is the value of any cofactor in the th row of (respectively, th column of ). In fact, the principal minor of obtained by deleting rows and columns indexed by equals the number of spanning forests of having precisely out-trees rooted at .
In all the approaches it is clear that the significant property of the Laplacian is that for . By allowing to be indeterminates over the field of rational numbers, the generating function version of the matrix tree theorem is obtained [a8], Sect. 3.3.25: The number of trees rooted at on the vertex set , with occurrences of the edge (directed away from the root), is the coefficient of the monomial in the th cofactor of the matrix , where and is the sum of the entries in the th row of , for .
Another direction of generalization is to interpret all the minors of the Laplacian rather than just the principal ones. Such generalizations can be found in [a5] and [a2], where arbitrary minors are expressed as signed sums over non-singular substructures that are more complicated than trees.
The edge version of the Laplacian is defined to be the -matrix . The connection of its cofactors with the Wiener index in applications to chemistry is presented in [a11]. The combinatorial description of the arbitrary minors of when is a tree is studied in [a1].
Applications are widespread. Variants of the matrix tree theorem are used in the topological analysis of passive electrical networks. The node-admittance matrix considered for this purpose is closely related to the Laplacian matrix (see [a10], Chap. 7). Abundance of forests suggests greater accessibility in networks. Due to this connection, the matrix tree theorem is used in developing distance concepts in social networks (see [a6]). The -matrix which occurs in the design of statistical experiments (cf. also Design of experiments) is the Laplacian of a graph associated with the design. In this context the matrix tree theorem is used to study -optimal designs (see [a7], p. 67). Finally, the matrix tree theorem is closely related to the Perron–Frobenius theorem. If is the transition matrix of an irreducible Markov chain, then by the Perron–Frobenius theorem it admits a unique stationary distribution. This fact is easily deduced from the matrix tree theorem, which in fact gives an interpretation of the components of the stationary distribution in terms of tree-counts. This observation is used to approximate the stationary distribution of a countable Markov chain (see [a15], p. 222).
An excellent survey of interesting developments related to Laplacians may be found in [a12].
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|[a16]||W.T. Tutte, "The disection of equilateral triangles into equilateral triangles" Proc. Cambridge Philos. Soc. , 44 (1948) pp. 463–482|
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Matrix tree theorem. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Matrix_tree_theorem&oldid=14639