Note on the Pair-crossing Number and the Odd-crossing Number

The crossing number of a graph G is the minimum possible number of edge-crossings in a drawing of G, the pair-crossing number is the minimum possible number of crossing pairs of edges in a drawing of G, and the odd-crossing number is the minimum number of pairs of edges that cross an odd number of times. Clearly, . We construct graphs with . This improves the bound of Pelsmajer, Schaefer and Štefankovič. Our construction also answers an old question of Tutte. Slightly improving the bound of Valtr, we also show that if the pair-crossing number of G is k, then its crossing number is at most O(k ²/log ² k). G. Tóth’s research was supported by the Hungarian Research Fund grant OTKA-K-60427 and the Research Foundation of the City University of New York.     

Tight Bounds for Connecting Sites Across Barriers

Given m points (sites) and n obstacles (barriers) in the plane, we address the problem of finding a straight line minimum cost spanning tree on the sites, where the cost is proportional to the number of intersections (crossings) between tree edges and barriers. If the barriers are infinite lines, it is known that there is a spanning tree such that every barrier is crossed by tree edges, and this bound is asymptotically optimal. Asano et al. showed that if the barriers are pairwise disjoint line segments, then there is a spanning tree such that every barrier crosses at most 4 tree edges and so the total cost is at most 4n. Lower bound constructions are known with 3 crossings per barrier and 2n total cost. We obtain tight bounds on the minimum cost of spanning trees in the special case where the barriers are interior disjoint line segments that form a convex subdivision of the plane and there is a point in every cell of the subdivision. In particular, we show that there is a spanning tree such that every barrier crosses at most 2 tree edges, and there is a spanning tree of total cost 5n/3. Both bounds are the best possible. Work by Eynat Rafalin and Diane Souvaine was supported by the National Science Foundation under Grant #CCF-0431027. E. Rafalin’s research conducted while at Tufts University.     

On edges crossing few other edges in simple topological complete graphs

We study the existence of edges having few crossings with the other edges in drawings of the complete graph (more precisely, in simple topological complete graphs). A topological graphT=(V,E) is a graph drawn in the plane with vertices represented by distinct points and edges represented by Jordan curves connecting the corresponding pairs of points (vertices), passing through no other vertices, and having the property that any intersection point of two edges is either a common end-point or a point where the two edges properly cross. A topological graph is simple if any two edges meet in at most one common point.Let h=h(n) be the smallest integer such that every simple topological complete graph on n vertices contains an edge crossing at most h other edges. We show that Ω(n^3/2)≤h(n)≤O(n²/log^1/4n). We also show that the analogous function on other surfaces (torus, Klein bottle) grows as cn².     

New Bounds on Crossing Numbers

The crossing number , cr(G) , of a graph G is the least number of crossing points in any drawing of G in the plane. Denote by κ(n,e) the minimum of cr(G) taken over all graphs with n vertices and at least e edges. We prove a conjecture of Erdos os and Guy by showing that κ(n,e)n ² /e ³ tends to a positive constant as n→∈fty and n l e l n ² . Similar results hold for graph drawings on any other surface of fixed genus. We prove better bounds for graphs satisfying some monotone properties. In particular, we show that if G is a graph with n vertices and e≥ 4n edges, which does not contain a cycle of length four (resp. six ), then its crossing number is at least ce ⁴ /n ³ (resp. ce ⁵ /n ⁴ ), where c>0 is a suitable constant. These results cannot be improved, apart from the value of the constant. This settles a question of Simonovits. Received January 27, 1999, and in revised form March 23, 1999.     

The Crossing Number of C(8, 2)□P n

C(n, k) is a graph obtained from n-cycle by adding edges v _i v _i+k (i = 1, 2,...,n, i + k (mod n)). There are several known results on the crossing numbers of the Cartesian products of C(n, k) (n ≤ 7) with paths, cycles and stars. In this paper we extend these results, and show that the crossing number of the Cartesian product of C(8, 2) with P _n is 8n. Yuanqiu Huang: Research supported by NSFC (10771062) and New Century Excellent Talents in University (NCET-07-0276). Jinwang Liu: Research supported by NSFC (10771058) and Hunan NSFC(O6jj20053).     

Approximating the fixed linear crossing number

We present a randomized polynomial-time approximation algorithm for the fixed linear crossing number problem (FLCNP). In this problem, the vertices of a graph are placed in a fixed order along a horizontal “node line” in the plane, each edge is drawn as an arc in one of the two half-planes (pages), and the objective is to minimize the number of edge crossings. FLCNP is NP-hard, and no previous polynomial-time approximation algorithms are known. We show that the problem can be generalized to k pages and transformed to the maximum k-cut problem which admits a randomized polynomial-time approximation. For the 2-page case, our approach leads to a randomized polynomial time 0.878+0.122ρ approximation algorithm for FLCNP, where ρ is the ratio of the number of conflicting pairs (pairs of edges that cross if drawn in the same page) to the crossing number. We further investigate this performance ratio on the random graph family G_n,1/2, where each edge of the complete graph K_n occurs with probability . We show that a longstanding conjecture for the crossing number of K_n implies that with probability at least 1-4e^-λ2, the expected performance bound of the algorithm on a random graph from G_n,1/2 is 1.366+O(λ/n). A series of experiments is performed to compare the algorithm against two other leading heuristics on a set of test graphs. The results indicate that the randomized algorithm yields near-optimal solutions and outperforms the other heuristics overall.     

Algorithms for graph partitioning on the planted partition model

The NP‐hard graph bisection problem is to partition the nodes of an undirected graph into two equal‐sized groups so as to minimize the number of edges that cross the partition. The more general graph l‐partition problem is to partition the nodes of an undirected graph into l equal‐sized groups so as to minimize the total number of edges that cross between groups. We present a simple, linear‐time algorithm for the graph l‐partition problem and we analyze it on a random “planted l‐partition” model. In this model, the n nodes of a graph are partitioned into l groups, each of size n/l; two nodes in the same group are connected by an edge with some probability p, and two nodes in different groups are connected by an edge with some probability r<p. We show that if p−r≥n^−1/2+ϵ for some constant ϵ, then the algorithm finds the optimal partition with probability 1− exp(−n^Θ(ε)). © 2001 John Wiley & Sons, Inc. Random Struct. Alg., 18: 116–140, 2001     

Topological Graphs with No Large Grids

Let G be a topological graph with n vertices, i.e., a graph drawn in the plane with edges drawn as simple Jordan curves. It is shown that, for any constants k,l, there exists another constant C(k,l), such that if G has at least C(k,l)n edges, then it contains a k×l-gridlike configuration, that is, it contains k+l edges such that each of the first k edges crosses each of the last l edges. Moreover, one can require the first k edges to be incident to the same vertex. Received: April, 2003 Janos Pach and Micha Sharir has been supported by NSF Grants CCR-97-32101 and CCR-00-98246, and by a joint grant from the U.S.–Israel Binational Science Foundation. János Pach has also been supported by PSC-CUNY Research Award 63382-0032 and by OTKA T-032452. Micha Sharir has also been supported by a grant from the Israeli Academy of Sciences for a Center of Excellence in Geometric Computing at Tel Aviv University, and by the Hermann Minkowski–MINERVA Center for Geometry at Tel Aviv University. Géza Tóth has been supported by OTKA-T-038397 and by an award from the New York University Research Challenge Fund.     

Crossing numbers of imbalanced graphs

The crossing number, cr(G), of a graph G is the least number of crossing points in any drawing of G in the plane. According to the Crossing Lemma of M. Ajtai, V. Chvátal, M. Newborn, E. Szemerédi, Theory and Practice of Combinatorics, North‐Holland, Amsterdam, New York, 1982, pp. 9–12 and F. T. Leighton, Complexity Issues in VLSI, MIT Press, Cambridge, 1983, the crossing number of any graph with n vertices and e>4n edges is at least constant times e³/n². Apart from the value of the constant, this bound cannot be improved. We establish some stronger lower bounds under the assumption that the distribution of the degrees of the vertices is irregular. In particular, we show that if the degrees of the vertices are d₁?d₂?···?d_n, then the crossing number satisfies \begin{eqnarray*}{\rm{cr}}(G)\geq \frac{c_{1}}{n}\end{eqnarray*} with \begin{eqnarray*}{\textstyle\sum\nolimits_{{{i}}={{{1}}}}^{{{n}}}}{{id}}_{{{i}}}^{{{3}}}-{{c}}_{{{2}}}{{n}}^{{{2}}}\end{eqnarray*}, and that this bound is tight apart from the values of the constants c₁, c₂>0. Some applications are also presented. © 2009 Wiley Periodicals, Inc. J Graph Theory 64: 12–21, 2010     

Minimum Clique Partition in Unit Disk Graphs

The minimum clique partition (MCP) problem is that of partitioning the vertex set of a given graph into a minimum number of cliques. Given n points in the plane, the corresponding unit disk graph (UDG) has these points as vertices, and edges connecting points at distance at most 1. MCP in UDGs is known to be NP-hard and several constant factor approximations are known, including a recent PTAS. We present two improved approximation algorithms for MCP in UDGs with a realization: (I) A polynomial time approximation scheme (PTAS) running in time n^O(1/e2){n^{O(1/\varepsilon^2)}}. This improves on a previous PTAS with n^O(1/e4){n^{O(1/\varepsilon^4)}} running time by Pirwani and Salavatipour (arXiv:0904.2203v1, 2009). (II) A randomized quadratic-time algorithm with approximation ratio 2.16. This improves on a ratio 3 algorithm with O(n ²) running time by Cerioli et al. (Electron. Notes Discret. Math. 18:73–79, 2004).     

Disjoint minimal graphs

We prove that the number s(n) of disjoint minimal graphs supported on domains in \mathbbRⁿ{\mathbb{R}^{n}} is bounded by e(n + 1)². In the two-dimensional case, we show that s(2) ≤ 3.     

Finding shortest paths in the plane in the presence of barriers to travel (for any lp - norm)

This paper develops an efficient algorithm for the computation of the shortest paths between given sets of points (origins and destinations) in the plane, when these paths are constrained not to cross any of a finite set of polygonal (open or closed) barriers. It is proved that when distances are measured by an 1_p - norm with 1 < p < ∞, these paths are formed by sequences straight line segments whose intermediate (e.g. apart from origin and destination) end points are barrier vertices. Moreover, only segments that locally support the barriers to which their end points belong are elligible for inclusion in a shortest path. The special case of one origin and one destination is considered, as well as the more general case of many origins and destinations. If n is the number of nodes (origins, destinations and barrier vertices), an algorithm is presented that builds that network of all shortest paths in O(n² log n) time. If the total number of edges in this network is e (bounded by n²), the application of Dijkstra's algorithm enables this computation of the shortest paths from any origin to all destinations in O(e log n) time. If the origins, shortest paths from all origins to all destinations can thus be found in O(ne log n) ≤ O(n^{3 log n}) time.It is also shown that optimal solutions when distances are measured according to the rectilinear or max-norm (i.e. l_p-norm with p = 1 or p = ∞) can be deduced from the results of the algorithm.     

Bounded Direction–Length Frameworks

A direction–length framework is a pair (G,p) where G=(V;D,L) is a ‘mixed’ graph whose edges are labelled as ‘direction’ or ‘length’ edges and p is a map from V to ℝ ^d for some d. The label of an edge uv represents a direction or length constraint between p(u) and p(v). Let G ⁺ be obtained from G by adding, for each length edge e of G, a direction edge with the same end vertices as e. We show that (G,p) is bounded if and only if (G ⁺,p) is infinitesimally rigid. This gives a characterization of when (G,p) is bounded in terms of the rank of the rigidity matrix of (G ⁺,p). We use this to characterize when a mixed graph is generically bounded in ℝ ^d . As an application we deduce that if (G,p) is a globally rigid generic framework with at least two length edges and e is a length edge of G then (G∖e,p) is bounded.     

Sum List Coloring Graphs

Let G=(V,E) be a graph with n vertices and e edges. The sum choice number of G is the smallest integer p such that there exist list sizes (f(v):v ∈ V) whose sum is p for which G has a proper coloring no matter which color lists of size f(v) are assigned to the vertices v. The sum choice number is bounded above by n+e. If the sum choice number of G equals n+e, then G is sum choice greedy. Complete graphs K_n are sum choice greedy as are trees. Based on a simple, but powerful, lemma we show that a graph each of whose blocks is sum choice greedy is also sum choice greedy. We also determine the sum choice number of K_2,n, and we show that every tree on n vertices can be obtained from K_n by consecutively deleting single edges where all intermediate graphs are sc-greedy.     

Rates of Convergence of Means of Euclidean Functionals

Let L be the Euclidean functional with p-th power-weighted edges. Examples include the sum of the p-th power-weighted lengths of the edges in minimal spanning trees, traveling salesman tours, and minimal matchings. Motivated by the works of Steele, Redmond and Yukich (Ann. Appl. Probab. 4, 1057–1073, 1994, Stoch. Process. Appl. 61, 289–304, 1996) have shown that for n i.i.d. sample points {X ₁,…,X _n } from [0,1] ^d , L({X ₁,…,X _n })/n ^(d−p)/d converges a.s. to a finite constant. Here we bound the rate of convergence of EL({X ₁,…,X _n })/n ^(d−p)/d . Y. Koo supported by the BK21 project of the Department of Mathematics, Sungkyunkwan University. S. Lee supported by the BK21 project of the Department of Mathematics, Yonsei University.     

Which Crossing Number Is It Anyway?

A drawing of a graph G is a mapping which assigns to each vertex a point of the plane and to each edge a simple continuous arc connecting the corresponding two points. The crossing number of G is the minimum number of crossing points in any drawing of G. We define two new parameters, as follows. The pairwise crossing number (resp. the odd-crossing number) of G is the minimum number of pairs of edges that cross (resp. cross an odd number of times) over all drawings of G. We prove that the largest of these numbers (the crossing number) cannot exceed, twice the square of the smallest (the odd-crossing number). Our proof is based on the following generalization of an old result of Hanani, which is of independent interest. Let G be a graph and let E₀ be a subset of its edges such that there is a drawing of G, in which every edge belonging to E₀ crosses any other edge an even number of times. Then g can be redrawn so that the elements of E₀ are not involved in any crossing. Finally, we show that the determination of each of these parameters is an NP-hard problem and it is NP-complete in the case of the crossing number and the odd-crossing number.     

On the Nullity of Bipartite Graphs

The nullity of a graph is the multiplicity of the eigenvalue zero in its spectrum. We obtain some lower bounds for the nullity of graphs and we then find the nullity of bipartite graphs with no cycle of length a multiple of 4 as a subgraph. Among bipartite graphs on n vertices, the star has the greatest nullity (equal to n − 2). We generalize this by showing that among bipartite graphs with n vertices, e edges and maximum degree Δ which do not have any cycle of length a multiple of 4 as a subgraph, the greatest nullity is . G. R. Omidi: This research was in part supported by a grant from IPM (No.87050016).     

On the long edges in the shortest tour throughN random points

Consider the shortest tour throughn pointsX ₁,...,X _n independently uniformly distributed over [0,1]². Then we show that for some universal constantK, the number of edges of length at leastun ^–1/2 is at mostKnxp(–u)²/K)with overwhelmingprobability.This research is in part supported by an NSF grant.     

A spectral heuristic for bisecting random graphs

The minimum bisection problem is to partition the vertices of a graph into two classes of equal size so as to minimize the number of crossing edges. Computing a minimum bisection is NP‐hard in the worst case. In this paper we study a spectral heuristic for bisecting random graphs G_n(p,p′) with a planted bisection obtained as follows: partition n vertices into two classes of equal size randomly, and then insert edges inside the two classes with probability p′ and edges crossing the partition with probability p independently. If , where c₀ is a suitable constant, then with probability 1 ? o(1) the heuristic finds a minimum bisection of G_n(p,p′) along with a certificate of optimality. Furthermore, we show that the structure of the set of all minimum bisections of G_n(p,p′) undergoes a phase transition as . The spectral heuristic solves instances in the subcritical, the critical, and the supercritical phases of the phase transition optimally with probability 1 ? o(1). These results extend previous work of Boppana 5 . © 2005 Wiley Periodicals, Inc. Random Struct. Alg., 2006