1,994 research outputs found

    International Conference on Discrete Mathematics (ICDM-2019)

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    Statistical Mechanics of maximal independent sets

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    The graph theoretic concept of maximal independent set arises in several practical problems in computer science as well as in game theory. A maximal independent set is defined by the set of occupied nodes that satisfy some packing and covering constraints. It is known that finding minimum and maximum-density maximal independent sets are hard optimization problems. In this paper, we use cavity method of statistical physics and Monte Carlo simulations to study the corresponding constraint satisfaction problem on random graphs. We obtain the entropy of maximal independent sets within the replica symmetric and one-step replica symmetry breaking frameworks, shedding light on the metric structure of the landscape of solutions and suggesting a class of possible algorithms. This is of particular relevance for the application to the study of strategic interactions in social and economic networks, where maximal independent sets correspond to pure Nash equilibria of a graphical game of public goods allocation

    On Constrained Intersection Representations of Graphs and Digraphs

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    We study the problem of determining minimal directed intersection representations of DAGs in a model introduced by [Kostochka, Liu, Machado, and Milenkovic, ISIT2019]: vertices are assigned color sets, two vertices are connected by an arc if and only if they share at least one color and the tail vertex has a strictly smaller color set than the head, and the goal is to minimize the total number of colors. We show that the problem is polynomially solvable in the class of triangle-free and Hamiltonian DAGs and also disclose the relationship of this problem with several other models of intersection representations of graphs and digraphs

    An extensive English language bibliography on graph theory and its applications

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    Bibliography on graph theory and its application

    Algorithmic Graph Theory

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    The main focus of this workshop was on mathematical techniques needed for the development of e๏ฌƒcient solutions and algorithms for computationally di๏ฌƒcult graph problems. The techniques studied at the workshhop included: the probabilistic method and randomized algorithms, approximation and optimization, structured families of graphs and approximation algorithms for large problems. The workshop Algorithmic Graph Theory was attended by 46 participants, many of them being young researchers. In 15 survey talks an overview of recent developments in Algorithmic Graph Theory was given. These talks were supplemented by 10 shorter talks and by two special sessions

    Hole์˜ ๊ด€์ ์—์„œ ๊ทธ๋ž˜ํ”„์™€ ์œ ํ–ฅ๊ทธ๋ž˜ํ”„์˜ ๊ตฌ์กฐ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์‚ฌ๋ฒ”๋Œ€ํ•™ ์ˆ˜ํ•™๊ต์œก๊ณผ,2019. 8. ๊น€์„œ๋ น.์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์œ ํ–ฅ๊ทธ๋ž˜ํ”„์™€ ๊ทธ๋ž˜ํ”„์˜ ํ™€์˜ ๊ด€์ ์—์„œ ๊ณ„ํ†ต๋ฐœ์ƒ ๊ทธ๋ž˜ํ”„์™€ ๊ทธ๋ž˜ํ”„์˜ ์‚ผ๊ฐํ™”์— ๋Œ€ํ•˜์—ฌ ์—ฐ๊ตฌํ•œ๋‹ค. ๊ธธ์ด 4 ์ด์ƒ์ธ ์œ ๋„๋œ ์‹ธ์ดํด์„ ํ™€์ด๋ผ ํ•˜๊ณ  ํ™€์ด ์—†๋Š” ๊ทธ๋ž˜ํ”„๋ฅผ ์‚ผ๊ฐํ™”๋œ ๊ทธ๋ž˜ํ”„๋ผ ํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์‹ธ์ดํด์„ ๊ฐ–์ง€ ์•Š๋Š” ์œ ํ–ฅ๊ทธ๋ž˜ํ”„์˜ ๊ณ„ํ†ต๋ฐœ์ƒ ๊ทธ๋ž˜ํ”„๊ฐ€ ์‚ผ๊ฐํ™”๋œ ๊ทธ๋ž˜ํ”„์ธ์ง€ ํŒ์ •ํ•˜๊ณ , ์ฃผ์–ด์ง„ ๊ทธ๋ž˜ํ”„๋ฅผ ์‚ผ๊ฐํ™”ํ•˜์—ฌ ํด๋ฆญ์ˆ˜๊ฐ€ ํฌ๊ฒŒ ์ฐจ์ด ๋‚˜์ง€ ์•Š๋Š” ๊ทธ๋ž˜ํ”„๋ฅผ ๋งŒ๋“œ๋Š” ๋ฐฉ๋ฒ•์„ ์ฐพ๊ณ ์ž ํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์€ ์—ฐ๊ตฌ ๋‚ด์šฉ์— ๋”ฐ๋ผ ๋‘ ๋ถ€๋ถ„์œผ๋กœ ๋‚˜๋‰œ๋‹ค. ๋จผ์ € (1,i)(1, i) ์œ ํ–ฅ๊ทธ๋ž˜ํ”„์™€ (i,1)(i, 1) ์œ ํ–ฅ๊ทธ๋ž˜ํ”„์˜ ๊ณ„ํ†ต๋ฐœ์ƒ ๊ทธ๋ž˜ํ”„๋ฅผ ์™„์ „ํ•˜๊ฒŒ ํŠน์ง•ํ™”ํ•˜๊ณ , (2,j)(2, j) ์œ ํ–ฅ๊ทธ๋ž˜ํ”„ DD์˜ ๋ชจ๋“  ์œ ํ–ฅ๋ณ€์—์„œ ๋ฐฉํ–ฅ์„ ์ œ๊ฑฐํ•œ ๊ทธ๋ž˜ํ”„๊ฐ€ ์‚ผ๊ฐํ™”๋œ ๊ทธ๋ž˜ํ”„์ด๋ฉด, DD์˜ ๊ณ„ํ†ต๋ฐœ์ƒ ๊ทธ๋ž˜ํ”„ ์—ญ์‹œ ์‚ผ๊ฐํ™”๋œ ๊ทธ๋ž˜ํ”„์ž„์„ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ์ ์€ ์ˆ˜์˜ ์‚ผ๊ฐํ˜•์„ ๊ฐ–๋Š” ์—ฐ๊ฒฐ๋œ ๊ทธ๋ž˜ํ”„์˜ ๊ณ„ํ†ต๋ฐœ์ƒ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•œ ์ •๋ฆฌ๋ฅผ ํ™•์žฅํ•˜์—ฌ ๋งŽ์€ ์ˆ˜์˜ ์‚ผ๊ฐํ˜•์„ ํฌํ•จํ•œ ์—ฐ๊ฒฐ๋œ ๊ทธ๋ž˜ํ”„์˜ ๊ณ„ํ†ต๋ฐœ์ƒ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๋‹ค๋ฅธ ํ•œ ํŽธ ๊ทธ๋ž˜ํ”„ GG์˜ ๋น„์‚ผ๊ฐํ™” ์ง€์ˆ˜ i(G)i(G)์— ๋Œ€ํ•˜์—ฌ ฯ‰(Gโˆ—)โˆ’ฯ‰(G)โ‰คi(G)\omega(G^*)-\omega(G) \le i(G)๋ฅผ ๋งŒ์กฑํ•˜๋Š” GG์˜ ์‚ผ๊ฐํ™”๋œ ๊ทธ๋ž˜ํ”„ Gโˆ—G^*๊ฐ€ ์กด์žฌํ•จ์„ ๋ณด์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ๋„๊ตฌ๋กœ ์ด์šฉํ•˜์—ฌ NC property๋ฅผ ๋งŒ์กฑํ•˜๋Š” ๊ทธ๋ž˜ํ”„๊ฐ€ Hadwiger ์ถ”์ธก๊ณผ Erd\H{o}s-Faber-Lov\'{a}sz ์ถ”์ธก์„ ๋งŒ์กฑํ•จ์„ ์ฆ๋ช…ํ•˜๊ณ , ๋น„์‚ผ๊ฐํ™” ์ง€์ˆ˜๊ฐ€ ์œ ๊ณ„์ธ ๊ทธ๋ž˜ํ”„๋“ค์ด linearly ฯ‡\chi-bounded์ž„์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค.This thesis aims at studying phylogeny graphs and graph completions in the aspect of holes of graphs or digraphs. A hole of a graph is an induced cycle of length at least four and a graph is chordal if it does not contain a hole. Specifically, we determine whether the phylogeny graphs of acyclic digraphs are chordal or not and find a way of chordalizing a graph without increasing the size of maximum clique not so much. In this vein, the thesis is divided into two parts. In the first part, we completely characterize phylogeny graphs of (1,i)(1, i) digraphs and (i,1)(i,1) digraphs, respectively, for a positive integer ii. Then, we show that the phylogeny graph of a (2,j)(2,j) digraph DD is chordal if the underlying graph of DD is chordal for any positive integer jj. In addition, we extend the existing theorems computing phylogeny numbers of connected graph with a small number of triangles to results computing phylogeny numbers of connected graphs with many triangles. In the second part, we present a minimal chordal supergraph Gโˆ—G^* of a graph GG satisfying the inequality ฯ‰(Gโˆ—)โˆ’ฯ‰(G)โ‰คi(G)\omega(G^*) - \omega(G) \le i(G) for the non-chordality index i(G)i(G) of GG. Using the above chordal supergraph as a tool, we prove that the family of graphs satisfying the NC property satisfies the Hadwiger conjecture and the Erd\H{o}s-Faber-Lov\'{a}sz Conjecture, and the family of graphs with bounded non-chordality indices is linearly ฯ‡\chi-bounded.Contents Abstract i 1 Introduction 1 1.1 Basic notions . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.1 Phylogeny graphs . . . . . . . . . . . . . . . . . . . . . 8 1.2.2 Graph colorings and chordal completions . . . . . . . . 14 2 Phylogeny graphs 19 2.1 Chordal phylogeny graphs . . . . . . . . . . . . . . . . . . . . 19 2.1.1 (1,j) phylogeny graphs and (i,1) phylogeny graphs . . 20 2.1.2 (2,j) phylogeny graphs . . . . . . . . . . . . . . . . . . 28 2.2 The phylogeny number and the triangles and the diamonds of a graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3 A new minimal chordal completion 61 3.1 Graphs with the NC property . . . . . . . . . . . . . . . . . . 64 3.2 The Erdห os-Faber-Lovรกsz Conjecture . . . . . . . . . . . . . . . 73 3.3 A minimal chordal completion of a graph . . . . . . . . . . . . 80 3.3.1 Non-chordality indices of graphs . . . . . . . . . . . . . 80 3.3.2 Making a local chordalization really local . . . . . . . . 89 3.4 New ฯ‡-bounded classes . . . . . . . . . . . . . . . . . . . . . . 97 Abstract (in Korean) 107Docto

    Combinatorial Optimization

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    This report summarizes the meeting on Combinatorial Optimization where new and promising developments in the field were discussed. Th
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