The boxicity of a graph G, denoted as box(G), is the minimum dimension d for which each vertex of G can be mapped to a d-dimensional axis-parallel box in Rd such that two boxes intersect if and only if the corresponding vertices of G are adjacent. An axis-parallel box is a generalized rectangle with sides parallel to the coordinate axes. If additionally, we restrict all sides of the rectangle to be of unit length, the new parameter so obtained is called the cubicity of the graph G, denoted by cub(G). F.S. Roberts had shown that for a graph G with n vertices, box(G) ≤ and cub(G) ≤ . A minimum vertex cover of a graph G is a minimum cardinality subset S of the vertex set of G such that each edge of G has at least one endpoint in S. We show that box(G) ≤ +1 and cub(G)≤ t+ ⌈log2(n −t)⌉−1 where t is the cardinality of a minimum vertex cover. Both these bounds are tight. For a bipartite graph G, we show that box(G) ≤ and this bound is tight. We observe that there exist graphs of very high boxicity but with very low chromatic num-ber. For example, there exist bipartite (2 colorable) graphs with boxicity equal to . Interestingly, if boxicity is very close to , then the chromatic number also has to be very high. In particular, we show that if box(G) = −s, s ≥ 02, then x(G) ≥ where X(G) is the chromatic number of G. We also discuss some known techniques for findingan upper boundon the boxicityof a graph -representing the graph as the intersection of graphs with boxicity 1 (boxicity 1 graphs are known as interval graphs) and covering the complement of the graph by co-interval graphs (a co-interval graph is the complement of an interval graph).

A graph Gis said to be an intersection graph of sets from a family of sets if there exists a function ƒ : V(G)→ such that for u,v V(G), (u,v) E(G) ƒ (u) ƒ (v) ≠ . Interval graphs are thus the intersection graphs of closed intervals on the real line and unit interval graphs are the intersection graphs of unit length intervals on the real line. An interval on the real line can be generalized to a “kbox” in Rk.A kbox B =(R1,R2,...,Rk), where each Riis a closed interval on the real line, is defined to be the Cartesian product R1x R2x…x Rk. If each Ri is a unit length interval, we call B a k-cube. Thus, 1-boxes are just closed intervals on the real line whereas 2-boxes are axis-parallel rectangles in the plane. We study the intersection graphs of k-boxes and k-cubes. The parameter boxicity of a graph G, denoted as box(G), is the minimum integer k such that G is an intersection graph of k-boxes. Similarly, the cubicity of G, denoted as cub(G), is the minimum integer k such that G is an intersection graph of k-cubes. Thus, interval graphs are the graphs with boxicity at most 1 and unit interval graphs are the graphs with cubicity at most 1. These parameters were introduced by F. S.Roberts in 1969. In some sense, the boxicity of a graph is a measure of how different a graph is from an interval graph and in a similar way, the cubicity is a measure of how different the graph is from a unit interval graph. We prove several upper bounds on the boxicity and cubicity of general as well as special classes of graphs in terms of various graph parameters such as the maximum degree, the number of vertices and the bandwidth. The following are some of the main results presented. 1. We show that for any graph G with maximum degree Δ , box(G)≤ Δ 22 . This result implies that bounded degree graphs have bounded boxicity no matter how large the graph might be. 2. It was shown in [18] that the boxicity of a graph on n vertices with maximum degree Δ is O(Δ ln n). But a similar bound does not hold for the average degree davof a graph. [18] gives graphs in which the boxicity is exponentially larger than davln n. We show that even though an O(davln n) upper bound for boxicity does not hold for all graphs, for almost all graphs, boxicity is O(davln n). 3. The ratio of the cubicity to boxicity of any graph shown in [15] when combined with the results on boxicity show that cub(G) is O(Δ ln 2 n) and O(2 ln n) for any graph G on n vertices and with maximum degree . By using a randomized construction, we prove the better upper bound cub(G) ≤ [4(Δ + 1) ln n.] 4. Two results relating the cubicity of a graph to its bandwidth b are presented. First, it is shown that cub(G) ≤ 12(Δ + 1)[ ln(2b)] + 1. Next, we derive the upper bound cub(G) ≤ b + 1. This bound is used to derive new upper bounds on the cubicity of special graph classes like circular arc graphs, cocomparability graphs and ATfree graphs in relation to the maximum degree. 5. The upper bound for cubicity in terms of the bandwidth gives an upper bound of Δ + 1 for the cubicity of interval graphs. This bound is improved to show that for any interval graph G with maximum degree , cub(G) ≤[ log2 Δ] + 4. 6. Scheinerman [54] proved that the boxicity of any outerplanar graph is at most 2. We present an independent proof for the same theorem. 7. Halin graphs are planar graphs formed by adding a cycle connecting the leaves of a tree none of whose vertices have degree 2. We prove that the boxicity of any Halin graph is equal to 2 unless it is a complete graph on 4 vertices, in which case its boxicity is 1.

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.

Let F be a family of sets. A graph G is an intersection graph of sets from the family F if there exists a mapping f : V (G)→ F such that, An interval graph is an intersection graph of a family of closed intervals on the real line. Interval graphs find application in diverse fields ranging from DNA analysis to VLSI design. An interval on the real line can be generalized to a k dimensional box or k-box. A k-box B = (R1.R2….Rk) is defined to be the Cartesian product R1 × R2 × …× Rk, where each Ri is a closed interval on the real line. If each Ri is a unit length interval, we call B a k-cube. Thus, an interval is a 1-box and a unit length interval is a 1-cube. A graph G has a k-box representation, if G is an intersection graph of a family of k-boxes in Rk. Similarly, G has a k-cube representation, if G is an intersection graph of a family of k-cubes in Rk. The boxicity of G, denoted by box(G), is the minimum positive integer k such that G has a k-box representation. Similarly, the cubicity of G, denoted by cub(G), is the minimum positive integer k such that G has a k-cube representation. Thus, interval graphs are the graphs with boxicity equal to 1 and unit interval graphs are the graphs with cubicity equal to 1. The concepts of boxicity and cubicity were introduced by F.S. Roberts in 1969. Deciding whether the boxicity (or cubicity) of a graph is at most k is NP-complete even for a small positive integer k. Box representation of graphs finds application in niche overlap (competition) in ecology and to problems of fleet maintenance in operations research. Given a low dimensional box representation, some well known NP-hard problems become polynomial time solvable. Attempts to find efficient box and cube representations for special classes of graphs can be seen in the literature. Scheinerman [6] showed that the boxicity of outerplanar graphs is at most 2. Thomassen [7] proved that the boxicity of planar graphs is bounded from above by 3. Cube representations of special classes of graphs like hypercubes and complete multipartite graphs were investigated in [5, 3, 4]. In this thesis, we present several bounds for boxicity and cubicity of special classes of graphs in terms of other graph parameters. The following are the main results shown in this work. 1. It was shown in [2] that, for a graph G with maximum degree Δ, cub(G) ≤ [4(Δ+ 1) log n]. We show that, for a k-degenerate graph G, cub(G) ≤ (k + 2)[2e log n]. Since k is at most Δ and can be much lower, this clearly is a stronger result. This bound is tight up to a constant factor. 2. For a k-degenerate graph G, we give an efficient deterministic algorithm that runs in O(n2k) time to output an O(k log n) dimensional cube representation. 3. Crossing number of a graph G is the minimum number of crossing pairs of edges, over all drawings of G in the plane. We show that if crossing number of G is t, then box(G) is O(t1/4 log3/4 t). This bound is tight up to a factor of O((log t)1/4 ). 4. We prove that almost all graphs have cubicity O(dav log n), where dav denotes the average degree. 5. Boxicity of a k-leaf power is at most k -1. For every k, there exist k-leaf powers whose boxicity is exactly k - 1. Since leaf powers are a subclass of strongly chordal graphs, this result implies that there exist strongly chordal graphs with arbitrarily high boxicity 6. Otachi et al. [8] conjectured that chordal bipartite graphs (CBGs) have boxicity at most 2. We disprove this conjecture by exhibiting an infinite family of CBGs that have unbounded boxicity. We first prove that the bipartite power of a tree (which is a CBG) is a CBG and then show that there exist trees whose bipartite powers have high boxicity. Later in Chapter ??, we prove a more generic result in bipartite powering. We prove that, for every k ≥ 3, the bipartite power of a bipartite, k-chordal graph is bipartite and k-chordal thus implying that CBGs are closed under bipartite powering. 7. Boxicity of a line graph with maximum degree Δ is O(Δ log2 log2 Δ). This is a log2 Δ log log Δ factor improvement over the best known upper bound for boxicity of any graph [1]. We also prove a non-trivial lower bound for the boxicity of a d-dimensional hypercube.

一個圖形G = (V,E)，如果可以找到最小k個和弦圖，則此圖形G = (V,E)的和弦圖數是k。 在這篇論文中，我們呈現存在一個系列平行圖的boxicity是3，且和弦圖數是1或2，存在一個平面圖形的和弦圖數是3。 / The chordality of G = (V,E) is dened as the minimum k such that we can write E = E1n...nEk, where each (V,Ei) is a chordal graph. In this thesis, we present that (1) there are series-parallel graphs with boxicity 3, (2) there are series-parallel graphs with chordality 1 or 2, and (3) there are planar graphs with chordality 3.

This thesis mainly focuses on algorithmic and combinatorial questions related to some geometric problems on graphs. In the last part of this thesis, a graph coloring problem is also discussed. Boxicity and Cubicity: These are graph parameters dealing with geomet-ric representations of graphs in higher dimensions. Both these parameters are known to be NP-Hard to compute in general and are even hard to approximate within an O(n1− ) factor for any > 0, under standard complexity theoretic assumptions. We studied algorithmic questions for these problems, for certain graph classes, to yield efficient algorithms or approximations. Our results include a polynomial time constant factor approximation algorithm for computing the cubicity of trees and a polynomial time constant (≤ 2.5) factor approximation algorithm for computing the boxicity of circular arc graphs. As far as we know, there were no constant factor approximation algorithms known previously, for computing boxicity or cubicity of any well known graph class for which the respective parameter value is unbounded. We also obtained parameterized approximation algorithms for boxicity with various edit distance parameters. An o(n) factor approximation algorithm for computing the boxicity and cubicity of general graphs also evolved as an interesting corollary of one of these parameterized algorithms. This seems to be the first sub-linear factor approximation algorithm known for computing the boxicity and cubicity of general graphs. Planar grid-drawings of outerplanar graphs: A graph is outerplanar, if it has a planar embedding with all its vertices lying on the outer face. We give an efficient algorithm to 2-vertex-connect any connected outerplanar graph G by adding more edges to it, in order to obtain a supergraph of G such that the resultant graph is still outerplanar and its pathwidth is within a constant times the pathwidth of G. This algorithm leads to a constant factor approximation algorithm for computing minimum height planar straight line grid-drawings of outerplanar graphs, extending the existing algorithm known for 2-vertex connected outerplanar graphs. n−1 3 Maximum matchings in triangle distance Delaunay graphs: Delau-nay graphs of point sets are well studied in Computational Geometry. Instead of the Euclidean metric, if the Delaunay graph is defined with respect to the convex distance function defined by an equilateral triangle, it is called a Trian-gle Distance Delaunay graph. TD-Delaunay graphs are known to be equivalent to geometric spanners called half-Θ6 graphs. It is known that classical Delaunay graphs of point sets always contain a near perfect matching, for non-degenerate point sets. We show that Triangle Distance Delaunay graphs of a set of n points in general position will always l m contain a matching of size and this bound is tight. We also show that Θ6 graphs, a class of supergraphs of half-Θ6 graphs, can have at most 5n − 11 edges, for point sets in general position. Heterochromatic Paths in Edge Colored Graphs: Conditions on the coloring to guarantee the existence of long heterochromatic paths in edge col-ored graphs is a well explored problem in literature. The objective here is to obtain a good lower bound for λ(G) - the length of a maximum heterochro-matic path in an edge-colored graph G, in terms of ϑ(G) - the minimum color degree of G under the given coloring. There are graph families for which λ(G) = ϑ(G) − 1 under certain colorings, and it is conjectured that ϑ(G) − 1 is a tight lower bound for λ(G). We show that if G has girth is at least 4 log2(ϑ(G))+2, then λ(G) ≥ ϑ(G)− 2. It is also proved that a weaker requirement that G just does not contain four-cycles is enough to guarantee that λ(G) is at least ϑ(G) −o(ϑ(G)). Other special cases considered include lower bounds for λ(G) in edge colored bipartite graphs, triangle-free graphs and graphs without heterochromatic triangles.

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.