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The Tutte Polynomial and Related Polynomials Lecture Notes 2010, 2012, 2014

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The Tutte Polynomial and Related Polynomials Lecture Notes 2010, 2012, 2014 The Tutte polynomial and related polynomials Lecture notes 2010, 2012, 2014 Andrew Goodall The following notes derive from three related series of lectures given for the Selected Chapters in Combinatorics course (Vybran´ekapitoly z kombinatoriky I) at the Computer Science Institute (IUUK)´ and the Department of Applied Mathematics (KAM) of the Faculty of Mathematics and Physics (MFF) at Charles University, Prague: \Many facets of the Tutte polynomial" (2010), \Graph invariants, homomorphisms and the Tutte polynomial" (2012), and \Duality in combinatorics: the examples of Tutte, Erd}os,and Ramsey" (2014).1 I have tried to make the notation as uniform as possible throughout and to avoid repetitions arising from overlaps between the three courses. However, I have left some background to cycles and cuts in Section 6 as it stands, rather than asking the reader to find the relevant material in Section 2. Contents 1 The chromatic polynomial 1 1.1 Graph-theoretic preliminaries . 1 1.2 The chromatic polynomial and proper colourings . 2 1.3 Deletion and contraction . 6 1.4 Subgraph expansions . 12 1.5 Some other deletion{contraction invariants. 15 2 Flows and tensions 17 2.1 Orientations . 17 2.2 Circuits and cocircuits . 18 2.3 The incidence matrix of an oriented graph . 21 2.4 A-flows and A-tensions . 23 2.5 Tensions and colourings . 26 2.6 Duality of bases for A-tensions and A-flows . 28 2.7 Examples of nowhere-zero flows . 29 2.8 The flow polynomial . 33 1The courses in 2012 and 2014 were complemented by lectures given by Prof. Jaroslav Neˇsetˇril,but the content of these lectures is not covered by the notes presented here. 1 3 The Tutte polynomial 35 3.1 Deletion-contraction recurrence . 35 3.2 Sugraph expansion of the Tutte polynomial . 42 3.3 Coefficients. Spanning tree expansion. 45 3.4 The Tutte polynomial of a planar graph . 48 3.5 The spanning tree partition of subgraphs. 50 3.6 The beta invariant. 51 3.7 Computational complexity . 54 3.8 The Laplacian and the number of spanning trees . 56 3.9 Hamming weight enumerator for tensions and flows . 57 3.10 Bicycles . 58 3.11 Z3-tension-flows . 61 4 The Tutte polynomial in statistical physics 64 4.1 Colourings and flows in the ice model . 64 4.2 The Potts model . 68 4.3 The Fortuin-Kasteleyn random cluster model . 69 5 Graph homomorphisms 71 5.1 Graph invariants and graph homomorphism profiles . 72 5.2 Homomorphism profiles determining graph invariants . 74 5.3 Spectrum and degree sequence by left profiles . 76 6 From graphs to matroids 77 6.1 Cuts, circuits and cycles . 78 6.2 Orthogonality of cycles and cutsets . 81 6.3 Graph duality . 82 6.4 Matroids . 84 6.5 Dual matroids . 88 6.6 Deletion and contraction . 89 7 Connections to knot theory 91 7.1 The medial of a plane graph . 91 7.2 Eulerian tours of digraphs . 92 7.3 2-in 2-out digraphs . 96 7.4 Interlace polynomial . 99 7.5 The Kauffman bracket of a link . 109 2 1 The chromatic polynomial 1.1 Graph-theoretic preliminaries Let G = (V; E) be a graph. A spanning subgraph is a subgraph (V; A) with A ⊆ E, and is denoted by GA. An induced subgraph is a subgraph (U; A), where A = fuv 2 E : u 2 U; v 2 Ug, and is denoted by G[U]. The number of connected components of G is denoted by c(G). A maximal spanning forest F is a forest which is a spanning subgraph of G with the property that F is contained in no other spanning forest of G, i.e., no edge of G can be added to F without creatng a cycle of G. A maximal spanning forest of a connected graph is a spanning tree. The rank r(G) = jV j − c(G) is the size of maximal spanning forest of G. Conversely, a spanning subgraph GA with c(GA) = c(G) is a maximal spanning forest of G. For A ⊆ E we often identify A with the spanning subgraph (V; A) and write r(A) for r(GA). So r(A) = jAj if and only if GA is a spanning forest; r(A) = r(E) if and only if GA has the same number of connected components as G; and r(A) = jAj = r(E) if and only if GA is a maximal spanning forest of G. The nullity n(G) = jEj − r(G) is the dimension of the cycle space of G (for a plane graph, this is the number of faces of G excepting the outer face). For A ⊆ E we set n(A) = n(GA). Deleting an edge e 2 E forms the spanning subgraph Gne = (V; Enfeg). Contracting an edge e = uv forms the graph G=e obtained by deleting e and then identifying the endpoints of e. An edge e is a bridge (isthmus, cut-edge, coloop) of G if r(Gne) < r(G), i.e., the number of connected components is increased upon removing e. An edge e is a bridge if and only if r(feg) = 1. An edge e = uv is a loop of G if u = v. Contracting a loop is the same as deleting it. An edge e is loop if and only if n(feg) = 1. An edge e is ordinary if it is neither a bridge nor a loop. 1.2 The chromatic polynomial and proper colourings There are various ways to define the chromatic polynomial P (G; z) of a graph G. Perhaps the first that springs to mind is to define it to be the graph invariant P (G; k) with the property that when k is a positive integer P (G; k) is the number of colourings of the vertices of G with k or fewer colours such that adjacent vertices receive different colours. One then has to prove that P (G; k) is indeed a polynomial in k. This can be done for example by an inclusion-exclusion argument, or by establishing that P (G; k) satisfies a deletion-contraction recurrence and using induction. However, we shall take an alternative approach and define a polynomial P (G; z) by specifying its coefficients as graph invariants that count what are called colour-partitions of the vertex set of G. It immediately emerges that P (G; k) does indeed count the proper vertex k-colourings of G. A further aspect of this approach is that we choose a basis different to the usual basis f1; z; z2;:::g for polynomials in z. This basis, f1; z; z(z−1);:::g, has the advantage that we are able to calculate the chromatic polynomial very easily for 3 many graphs, such as complete multipartite graphs. The chromatic polynomial has been the subject of intensive study ever since Birkhoff introduced it in 1912 [8], perhaps with an analytic approach to the Four Colour Conjecture in mind. Although such an approach has not led to such a proof of the Four Colour Conjecture being found, study of the chromatic polynomial has led to many advances in graph theory that might not otherwise have ben made. The chromatic polynomial played a significant role historically in Tutte's elucidation of tension-flow duality. (Later we look at Tutte's eponymous polynomial, introduced as simultaneous generalization of the chromatic and flow polynomials.) More about graph colourings can be found in e.g. [10, ch. V], [17, ch. 5], and more about the chromatic polynomial in e.g. [7, ch. 9] and [20]. We approach the chromatic polynomial via the key property that vertices of the same colour in a proper colouring of G form an independent (stable) set in G. Definition 1.1. A colour-partition of a graph G = (V; E) is a partition of V into disjoint non-empty subsets, V = V1 [ V2 [···[ Vk, such that the colour-class Vi is an independent set of vertices in G, for each 1 ≤ i ≤ k (i.e., each induced subgraph G[Vi] has no edges). The chromatic number χ(G) is the least natural number k for which such a partition is possible. If G has a loop then it has no colour-partitions. Adding or removing edges in parallel to a given edge makes no difference to what counts as a colour-partition, since its definition depends only on whether vertices are adjacent or not. We denote the falling factorial z(z − 1) ··· (z − i + 1) by zi. Definition 1.2. Let G = (V; E) be a graph and let ai(G) denote the number of colour- partitions of G into i colour-classes. The chromatic polynomial of G is defined by XjV j i P (G; z) = ai(G)z : i=1 For example, when G is the complete graph on n vertices, n P (Kn; z) = z = z(z − 1) ··· (z − n + 1); with ai(Kn) = 0 for 1 ≤ i ≤ n − 1 and an(Kn) = 1. If G has n vertices then an(G) = 1 so that P (G; z) has leading coefficient 1. The constant term P (G; 0) is zero since z is a factor of zi for each 1 ≤ i ≤ n. If E is non- empty then P (G; 1) = 0, so that z − 1 is a factor of P (G; z). More generally, the integers 0; 1; : : : ; χ(G) − 1 are all roots of P (G; z), and χ(G) is the first positive integer that is not a root of P (G; z). Proposition 1.3. If G = (V; E) is a simple graph on n vertices and m edges then the coefficient of zn−1 in P (G; z) is equal to −m.
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