Generalizing Cographs to 2-Cographs
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Forbidden Subgraph Characterization of Quasi-Line Graphs Medha Dhurandhar [email protected]
Forbidden Subgraph Characterization of Quasi-line Graphs Medha Dhurandhar [email protected] Abstract: Here in particular, we give a characterization of Quasi-line Graphs in terms of forbidden induced subgraphs. In general, we prove a necessary and sufficient condition for a graph to be a union of two cliques. 1. Introduction: A graph is a quasi-line graph if for every vertex v, the set of neighbours of v is expressible as the union of two cliques. Such graphs are more general than line graphs, but less general than claw-free graphs. In [2] Chudnovsky and Seymour gave a constructive characterization of quasi-line graphs. An alternative characterization of quasi-line graphs is given in [3] stating that a graph has a fuzzy reconstruction iff it is a quasi-line graph and also in [4] using the concept of sums of Hoffman graphs. Here we characterize quasi-line graphs in terms of the forbidden induced subgraphs like line graphs. We consider in this paper only finite, simple, connected, undirected graphs. The vertex set of G is denoted by V(G), the edge set by E(G), the maximum degree of vertices in G by Δ(G), the maximum clique size by (G) and the chromatic number by G). N(u) denotes the neighbourhood of u and N(u) = N(u) + u. For further notation please refer to Harary [3]. 2. Main Result: Before proving the main result we prove some lemmas, which will be used later. Lemma 1: If G is {3K1, C5}-free, then either 1) G ~ K|V(G)| or 2) If v, w V(G) are s.t. -
Infinitely Many Minimal Classes of Graphs of Unbounded Clique-Width∗
Infinitely many minimal classes of graphs of unbounded clique-width∗ A. Collins, J. Foniok†, N. Korpelainen, V. Lozin, V. Zamaraev Abstract The celebrated theorem of Robertson and Seymour states that in the family of minor-closed graph classes, there is a unique minimal class of graphs of unbounded tree-width, namely, the class of planar graphs. In the case of tree-width, the restriction to minor-closed classes is justified by the fact that the tree-width of a graph is never smaller than the tree-width of any of its minors. This, however, is not the case with respect to clique-width, as the clique-width of a graph can be (much) smaller than the clique-width of its minor. On the other hand, the clique-width of a graph is never smaller than the clique-width of any of its induced subgraphs, which allows us to be restricted to hereditary classes (that is, classes closed under taking induced subgraphs), when we study clique-width. Up to date, only finitely many minimal hereditary classes of graphs of unbounded clique-width have been discovered in the literature. In the present paper, we prove that the family of such classes is infinite. Moreover, we show that the same is true with respect to linear clique-width. Keywords: clique-width, linear clique-width, hereditary class 1 Introduction Clique-width is a graph parameter which is important in theoretical computer science, because many algorithmic problems that are generally NP-hard become polynomial-time solvable when restricted to graphs of bounded clique-width [4]. -
Networkx: Network Analysis with Python
NetworkX: Network Analysis with Python Salvatore Scellato Full tutorial presented at the XXX SunBelt Conference “NetworkX introduction: Hacking social networks using the Python programming language” by Aric Hagberg & Drew Conway Outline 1. Introduction to NetworkX 2. Getting started with Python and NetworkX 3. Basic network analysis 4. Writing your own code 5. You are ready for your project! 1. Introduction to NetworkX. Introduction to NetworkX - network analysis Vast amounts of network data are being generated and collected • Sociology: web pages, mobile phones, social networks • Technology: Internet routers, vehicular flows, power grids How can we analyze this networks? Introduction to NetworkX - Python awesomeness Introduction to NetworkX “Python package for the creation, manipulation and study of the structure, dynamics and functions of complex networks.” • Data structures for representing many types of networks, or graphs • Nodes can be any (hashable) Python object, edges can contain arbitrary data • Flexibility ideal for representing networks found in many different fields • Easy to install on multiple platforms • Online up-to-date documentation • First public release in April 2005 Introduction to NetworkX - design requirements • Tool to study the structure and dynamics of social, biological, and infrastructure networks • Ease-of-use and rapid development in a collaborative, multidisciplinary environment • Easy to learn, easy to teach • Open-source tool base that can easily grow in a multidisciplinary environment with non-expert users -
Counting Independent Sets in Graphs with Bounded Bipartite Pathwidth∗
Counting independent sets in graphs with bounded bipartite pathwidth∗ Martin Dyery Catherine Greenhillz School of Computing School of Mathematics and Statistics University of Leeds UNSW Sydney, NSW 2052 Leeds LS2 9JT, UK Australia [email protected] [email protected] Haiko M¨uller∗ School of Computing University of Leeds Leeds LS2 9JT, UK [email protected] 7 August 2019 Abstract We show that a simple Markov chain, the Glauber dynamics, can efficiently sample independent sets almost uniformly at random in polynomial time for graphs in a certain class. The class is determined by boundedness of a new graph parameter called bipartite pathwidth. This result, which we prove for the more general hardcore distribution with fugacity λ, can be viewed as a strong generalisation of Jerrum and Sinclair's work on approximately counting matchings, that is, independent sets in line graphs. The class of graphs with bounded bipartite pathwidth includes claw-free graphs, which generalise line graphs. We consider two further generalisations of claw-free graphs and prove that these classes have bounded bipartite pathwidth. We also show how to extend all our results to polynomially-bounded vertex weights. 1 Introduction There is a well-known bijection between matchings of a graph G and independent sets in the line graph of G. We will show that we can approximate the number of independent sets ∗A preliminary version of this paper appeared as [19]. yResearch supported by EPSRC grant EP/S016562/1 \Sampling in hereditary classes". zResearch supported by Australian Research Council grant DP190100977. 1 in graphs for which all bipartite induced subgraphs are well structured, in a sense that we will define precisely. -
1 Hamiltonian Path
6.S078 Fine-Grained Algorithms and Complexity MIT Lecture 17: Algorithms for Finding Long Paths (Part 1) November 2, 2020 In this lecture and the next, we will introduce a number of algorithmic techniques used in exponential-time and FPT algorithms, through the lens of one parametric problem: Definition 0.1 (k-Path) Given a directed graph G = (V; E) and parameter k, is there a simple path1 in G of length ≥ k? Already for this simple-to-state problem, there are quite a few radically different approaches to solving it faster; we will show you some of them. We’ll see algorithms for the case of k = n (Hamiltonian Path) and then we’ll turn to “parameterizing” these algorithms so they work for all k. A number of papers in bioinformatics have used quick algorithms for k-Path and related problems to analyze various networks that arise in biology (some references are [SIKS05, ADH+08, YLRS+09]). In the following, we always denote the number of vertices jV j in our given graph G = (V; E) by n, and the number of edges jEj by m. We often associate the set of vertices V with the set [n] := f1; : : : ; ng. 1 Hamiltonian Path Before discussing k-Path, it will be useful to first discuss algorithms for the famous NP-complete Hamiltonian path problem, which is the special case where k = n. Essentially all algorithms we discuss here can be adapted to obtain algorithms for k-Path! The naive algorithm for Hamiltonian Path takes time about n! = 2Θ(n log n) to try all possible permutations of the nodes (which can also be adapted to get an O?(k!)-time algorithm for k-Path, as we’ll see). -
General Approach to Line Graphs of Graphs 1
DEMONSTRATIO MATHEMATICA Vol. XVII! No 2 1985 Antoni Marczyk, Zdzislaw Skupien GENERAL APPROACH TO LINE GRAPHS OF GRAPHS 1. Introduction A unified approach to the notion of a line graph of general graphs is adopted and proofs of theorems announced in [6] are presented. Those theorems characterize five different types of line graphs. Both Krausz-type and forbidden induced sub- graph characterizations are provided. So far other authors introduced and dealt with single spe- cial notions of a line graph of graphs possibly belonging to a special subclass of graphs. In particular, the notion of a simple line graph of a simple graph is implied by a paper of Whitney (1932). Since then it has been repeatedly introduc- ed, rediscovered and generalized by many authors, among them are Krausz (1943), Izbicki (1960$ a special line graph of a general graph), Sabidussi (1961) a simple line graph of a loop-free graph), Menon (1967} adjoint graph of a general graph) and Schwartz (1969; interchange graph which coincides with our line graph defined below). In this paper we follow another way, originated in our previous work [6]. Namely, we distinguish special subclasses of general graphs and consider five different types of line graphs each of which is defined in a natural way. Note that a similar approach to the notion of a line graph of hypergraphs can be adopted. We consider here the following line graphsi line graphs, loop-free line graphs, simple line graphs, as well as augmented line graphs and augmented loop-free line graphs. - 447 - 2 A. Marczyk, Z. -
K-Path Centrality: a New Centrality Measure in Social Networks
Centrality Metrics in Social Network Analysis K-path: A New Centrality Metric Experiments Summary K-Path Centrality: A New Centrality Measure in Social Networks Adriana Iamnitchi University of South Florida joint work with Tharaka Alahakoon, Rahul Tripathi, Nicolas Kourtellis and Ramanuja Simha Adriana Iamnitchi K-Path Centrality: A New Centrality Measure in Social Networks 1 of 23 Centrality Metrics in Social Network Analysis Centrality Metrics Overview K-path: A New Centrality Metric Betweenness Centrality Experiments Applications Summary Computing Betweenness Centrality Centrality Metrics in Social Network Analysis Betweenness Centrality - how much a node controls the flow between any other two nodes Closeness Centrality - the extent a node is near all other nodes Degree Centrality - the number of ties to other nodes Eigenvector Centrality - the relative importance of a node Adriana Iamnitchi K-Path Centrality: A New Centrality Measure in Social Networks 2 of 23 Centrality Metrics in Social Network Analysis Centrality Metrics Overview K-path: A New Centrality Metric Betweenness Centrality Experiments Applications Summary Computing Betweenness Centrality Betweenness Centrality measures the extent to which a node lies on the shortest path between two other nodes betweennes CB (v) of a vertex v is the summation over all pairs of nodes of the fractional shortest paths going through v. Definition (Betweenness Centrality) For every vertex v 2 V of a weighted graph G(V ; E), the betweenness centrality CB (v) of v is defined by X X σst (v) CB -
The Strong Perfect Graph Theorem
Annals of Mathematics, 164 (2006), 51–229 The strong perfect graph theorem ∗ ∗ By Maria Chudnovsky, Neil Robertson, Paul Seymour, * ∗∗∗ and Robin Thomas Abstract A graph G is perfect if for every induced subgraph H, the chromatic number of H equals the size of the largest complete subgraph of H, and G is Berge if no induced subgraph of G is an odd cycle of length at least five or the complement of one. The “strong perfect graph conjecture” (Berge, 1961) asserts that a graph is perfect if and only if it is Berge. A stronger conjecture was made recently by Conforti, Cornu´ejols and Vuˇskovi´c — that every Berge graph either falls into one of a few basic classes, or admits one of a few kinds of separation (designed so that a minimum counterexample to Berge’s conjecture cannot have either of these properties). In this paper we prove both of these conjectures. 1. Introduction We begin with definitions of some of our terms which may be nonstandard. All graphs in this paper are finite and simple. The complement G of a graph G has the same vertex set as G, and distinct vertices u, v are adjacent in G just when they are not adjacent in G.Ahole of G is an induced subgraph of G which is a cycle of length at least 4. An antihole of G is an induced subgraph of G whose complement is a hole in G. A graph G is Berge if every hole and antihole of G has even length. A clique in G is a subset X of V (G) such that every two members of X are adjacent. -
Cut and Pendant Vertices and the Number of Connected Induced Subgraphs of a Graph
CUT AND PENDANT VERTICES AND THE NUMBER OF CONNECTED INDUCED SUBGRAPHS OF A GRAPH AUDACE A. V. DOSSOU-OLORY Abstract. A vertex whose removal in a graph G increases the number of components of G is called a cut vertex. For all n; c, we determine the maximum number of connected induced subgraphs in a connected graph with order n and c cut vertices, and also charac- terise those graphs attaining the bound. Moreover, we show that the cycle has the smallest number of connected induced subgraphs among all cut vertex-free connected graphs. The general case c > 0 remains an open task. We also characterise the extremal graph struc- tures given both order and number of pendant vertices, and establish the corresponding formulas for the number of connected induced subgraphs. The `minimal' graph in this case is a tree, thus coincides with the structure that was given by Li and Wang [Further analysis on the total number of subtrees of trees. Electron. J. Comb. 19(4), #P48, 2012]. 1. Introduction and Preliminaries Let G be a simple graph with vertex set V (G) and edge set E(G). The graph G is said to be connected if for all u; v 2 V (G), there is a u − v path in G. An induced subgraph H of G is a graph such that ;= 6 V (H) ⊆ V (G) and E(H) consists of all those edges of G whose endvertices both belong to V (H). The order of G is the cardinality jV (G)j, i.e. the number of vertices of G; the girth of G is the smallest order of a cycle (if any) in G; a pendant vertex (or leaf) of G is a vertex of degree 1 in G. -
Induced Cycles in Graphs
Induced Cycles in Graphs 1Michael A. Henning, 2Felix Joos, 2Christian L¨owenstein, and 2Thomas Sasse 1Department of Mathematics University of Johannesburg Auckland Park, 2006 South Africa E-mail: [email protected] 2Institute of Optimization and Operations Research, Ulm University, Ulm 89081, Germany, E-mail: [email protected], E-mail: [email protected], E-mail: [email protected] Abstract The maximum cardinality of an induced 2-regular subgraph of a graph G is denoted by cind(G). We prove that if G is an r-regular graph of order n, then cind(G) ≥ n 1 2(r−1) + (r−1)(r−2) and we prove that if G is a cubic claw-free graph on order n, then cind(G) > 13n/20 and this bound is asymptotically best possible. Keywords: Induced regular subgraph; induced cycle; claw-free; matching; 1-extendability AMS subject classification: 05C38, 05C69 arXiv:1406.0606v1 [math.CO] 3 Jun 2014 1 Introduction The problem of finding a largest induced r-regular subgraph of a given graph for any value of r ≥ 0 has attracted much interest and dates back to Erd˝os, Fajtlowicz, and Staton [2]. Cardoso et al. [1] showed that it is NP-hard to find a maximum induced r-regular subgraph of a given graph. Lozin et al. [7] established efficient algorithms for special graph classes including 2P3-free graphs, while Moser and Thilikos [8] studied FPT-algorithms for finding regular induced subgraphs. The special case of finding a largest induced r-regular subgraph when r = 0 is the well- studied problem of finding a maximum independent set in a graph. -
The On-Line Shortest Path Problem Under Partial Monitoring
Journal of Machine Learning Research 8 (2007) 2369-2403 Submitted 4/07; Revised 8/07; Published 10/07 The On-Line Shortest Path Problem Under Partial Monitoring Andras´ Gyor¨ gy [email protected] Machine Learning Research Group Computer and Automation Research Institute Hungarian Academy of Sciences Kende u. 13-17, Budapest, Hungary, H-1111 Tamas´ Linder [email protected] Department of Mathematics and Statistics Queen’s University, Kingston, Ontario Canada K7L 3N6 Gabor´ Lugosi [email protected] ICREA and Department of Economics Universitat Pompeu Fabra Ramon Trias Fargas 25-27 08005 Barcelona, Spain Gyor¨ gy Ottucsak´ [email protected] Department of Computer Science and Information Theory Budapest University of Technology and Economics Magyar Tudosok´ Kor¨ utja´ 2. Budapest, Hungary, H-1117 Editor: Leslie Pack Kaelbling Abstract The on-line shortest path problem is considered under various models of partial monitoring. Given a weighted directed acyclic graph whose edge weights can change in an arbitrary (adversarial) way, a decision maker has to choose in each round of a game a path between two distinguished vertices such that the loss of the chosen path (defined as the sum of the weights of its composing edges) be as small as possible. In a setting generalizing the multi-armed bandit problem, after choosing a path, the decision maker learns only the weights of those edges that belong to the chosen path. For this problem, an algorithm is given whose average cumulative loss in n rounds exceeds that of the best path, matched off-line to the entire sequence of the edge weights, by a quantity that is proportional to 1=pn and depends only polynomially on the number of edges of the graph. -
Dominating Cliques in Graphs
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Discrete Mathematics 86 (1990) 101-116 101 North-Holland DOMINATING CLIQUES IN GRAPHS Margaret B. COZZENS Mathematics Department, Northeastern University, Boston, MA 02115, USA Laura L. KELLEHER Massachusetts Maritime Academy, USA Received 2 December 1988 A set of vertices is a dominating set in a graph if every vertex not in the dominating set is adjacent to one or more vertices in the dominating set. A dominating clique is a dominating set that induces a complete subgraph. Forbidden subgraph conditions sufficient to imply the existence of a dominating clique are given. For certain classes of graphs, a polynomial algorithm is given for finding a dominating clique. A forbidden subgraph characterization is given for a class of graphs that have a connected dominating set of size three. Introduction A set of vertices in a simple, undirected graph is a dominating set if every vertex in the graph which is not in the dominating set is adjacent to one or more vertices in the dominating set. The domination number of a graph G is the minimum number of vertices in a dominating set. Several types of dominating sets have been investigated, including independent dominating sets, total dominating sets, and connected dominating sets, each of which has a corresponding domination number. Dominating sets have applications in a variety of fields, including communication theory and political science. For more background on dominating sets see [3, 5, 151 and other articles in this issue. For arbitrary graphs, the problem of finding the size of a minimum dominating set in the graph is an NP-complete problem [9].