This thesis is separated into two parts: threshold graphs and inertia sets. First we present an algorithmic approach to finding the minimum rank of threshold graphs and then progress to counting the number of threshold graphs with a specific minimum rank. Second, we find an algorithmic and more automated way of determining the inertia set of graphs with seven or fewer vertices using theorems and lemmata found in previous papers. Inertia sets are a relaxation of the inverse eigenvalue problem. Instead of determining all the possible eigenvalues that can be obtained by matrices with a specific zero/nonzero pattern we restrict to counting the number of positive and negative eigenvalues.

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Let G be a simple graph on n vertices, and let S(G) be the class of all real-valued symmetric nxn matrices whose nonzero off-diagonal entries occur in exactly the positions corresponding to the edges of G. The smallest rank achieved by a matrix in S(G) is called the minimum rank of G, denoted mr(G). The maximum nullity achieved by a matrix in S(G) is denoted M(G). For each graph G, there is an associated minimum rank class, MR(G) consisting of all matrices A in S(G) with rank A = mr(G). Although no restrictions are applied to the diagonal entries of matrices in S(G), sometimes diagonal entries corresponding to specific vertices of G must be zero for all matrices in MR(G). These vertices are known as nil vertices (see [6]). In this paper I discuss some basic results about nil vertices in general and nil vertices in cographs and prove that cographs with a nil vertex of a particular form contain two other nil vertices symmetric to the first. I discuss several open questions relating to these results and a counterexample. I prove that for all cographs G without an induced complete tripartite graph with independent sets all of size 3, the zero-forcing number Z(G), a graph theoretic parameter, is equal to M(G). In fact this result holds for a slightly larger class of cographs and in particular holds for all threshold graphs. Lastly, I prove that the maximum of the minimum ranks of all cographs on n vertices is the floor of 2n/3.

In this thesis we study the following dimensional parameters : boxicity, cubicity, threshold dimension and poset dimension. While the first three parameters are defined on graphs, poset dimension is defined on partially ordered sets (or posets). We only consider finite graphs and posets. In addition, we assume that the graphs are simple and undirected. Boxicity and Cubicity: A k-box (k-cube) is a Cartesian product of closed intervals(unit-intervals) [a1,b1]x…x [ak,bk]. The boxicity (cubicity) of a graph G,box (G) (cub(G)) is the minimum integer k such that every vertex in G is mapped to a k-box(k-cube) in the k-dimensional Euclidean space and two boxes(cubes) intersect if and only if their corresponding vertices are adjacent in G. Boxicity and cubicity can be considered as extensions of the concept of interval graphs and unit-interval graphs respectively. Threshold Dimension: A graph G is a threshold graph if there is a real number p and a weight function w: V→ R such that for any two vertices u,,v ε V(G),{ u, v }is an edge if and only if w(u)+w(v) ≥ p. The threshold dimension of a graph G is the minimum integer k such that there exist k threshold graphs Gi, i =1,2,...,k which satisfy E(G)= E(G1)U E(G2)U….UE(Gk). Poset Dimension: Let P = (S, P)be a poset where S is a finite non-empty set and P is a reflexive, anti-symmetric and transitive binary relation on S. P is a total order if every pair of elements in S is comparable in P. The dimension of P , denoted by dim(P )is the minimum integer k such that there exist k total orders on S, L1,...,Lk and for two distinct elements x,y ε S: x < y in P if and only if x < y in each Li,i ε ,{1. 2,...,k } All the four dimensional parameters that we have considered are very hard to compute. It is NP-complete to even determine if the boxicity of a graph is at most 2, if its cubicity is at most 3, if its threshold dimension is at most 3 and if the dimension of a poset is at most 3. Also it is hard to design an approximation algorithm within √n factor for computing the dimension of a poset. OurResults We state some of our main results: 1. Lower bounds for boxicity: We have developed two general methods based on certain vertex isoperimetric properties of graphs for deriving lower bounds. Application of these methods has led to some significant results. We mention a few of them here: ( a) Almost all graphs have boxicity Ω(n). (b) For a fixed k, boxicity of random k-regular graphs is Ω(k/log k). 2. Consider a poset P = (S,P) and let GP be its underlying comparability graph. We show that for any poset P, box(GP)/(χ(GP) - 1) ≤ dim(P) ≤ 2box (GP), where χ(GP) is the chromatic number of GP and χ(GP) = 1. Some important consequences of this result are: (a) It allows us to derive hitherto unknown upper bounds for poset dimension such as dim(P) ≤ 2tree-width (GP) + 4. (b) The boxicity of any graph with maximum degree Δ is O (Δlog2 Δ) which is an improvement over the best known upper bound of Δ2 +2. (c) There exist graphs with boxicity Ω(ΔlogΔ). This disproves a conjecture that the boxicity of a graph is O(Δ). (d)There exists no polynomial-time algorithm to approximate the boxicity of a bipartite graph on n vertices within a factor of O(n0.5−ε)for any ε > 0, unless NP = ZPP. 3.We show that every poset can be associated with a split graph such that the threshold dimension of the complement of the split graph is equal to the dimension of the poset. As a consequence we show that there exists no polynomial-time algorithm to approximate the threshold dimension of a split graph on n vertices with a factor of O(n0.5−ε)for any ε > 0, unless NP= ZPP. 4.We have given an upper bound for the cubicity of interval graphs. Claw number of a graph G, ψ(G) is the largest positive integer m such that K1,m is an induced subgraph of G. If G is an interval graph, we show that [log2 ψ(G)] ≤ cub(G) ≤ min([log2 α ], [log2 ψ(G)] +2), where α is the independence number of G. 5.We have improved upper bounds for the dimension of incidence posets and interval orders which are among the well-studied classes of posets.

This thesis touches three different topics in graph theory, namely, rainbow colouring, product dimension and boxicity. Rainbow colouring An edge colouring of a graph is called a rainbow colouring, if every pair of vertices is connected by atleast one path in which no two edges are coloured the same. The rainbow connection number of a graph is the minimum number of colours required to rainbow colour it. In this thesis we give upper bounds on rainbow connection number based on graph invariants like minimum degree, vertex connectivity, and radius. We also give some computational complexity results for special graph classes. Product dimension The product dimension or Prague dimension of a graph G is the smallest natural number k such that G is an induced subgraph of a direct product of k complete graphs. In this thesis, we give upper bounds on the product dimension for forests, bounded tree width graphs and graphs of bounded degeneracy. Boxicity and cubicity The boxicity (cubicity of a graph G is the smallest natural number k such that G can be represented as an intersection graph of axis-parallel rectangular boxes(axis-parallel unit cubes) in Rk .In this thesis, we study the boxicity and the cubicity of Cartesian, strong and direct products of graphs and give estimates on the boxicity and the cubicity of a product graph based on invariants of the component graphs. Separation dimension The separation dimension of a hypergraph H is the smallest natural number k for which the vertices of H can be embedded in Rk such that any two disjoint edges of H can be separated by a hyper plane normal to one of the axes. While studying the boxicity of line graphs, we noticed that a box representation of the line graph of a hypergraph has a nice geometric interpretation. Hence we introduced this new parameter and did an extensive study of the same.