KONSEP DASAR HIPERGRAF DAN SIFAT-SIFATNYA

This article discusses fundamental properties of hypergraphs. Hypergraphs are generalization of graph which hyperedges, edges in hypergraph, can join more than two vertices. The fundamental properties in this article are the vertices degrees, connection in hypergraphs, and dual hypergraph. connectivity in hypergraphs in this article are walks, trails, strict trails, path, and cycles. In the end of this article, we present a few examples of problems that can be represented by hypergraph

On the Complexity of Community-aware Network Sparsification

Network sparsification is the task of reducing the number of edges of a given graph while preserving some crucial graph property. In community-aware network sparsification, the preserved property concerns the subgraphs that are induced by the communities of the graph which are given as vertex subsets. This is formalized in the $\Pi$-Network Sparsification problem: given an edge-weighted graph $G$, a collection $Z$ of $c$ subsets of $V(G)$ (communities), and two numbers $\ell, b$, the question is whether there exists a spanning subgraph $G'$ of $G$ with at most $\ell$ edges of total weight at most $b$ such that $G'[C]$ fulfills $\Pi$ for each community $C$. Here, we consider two graph properties $\Pi$: the connectivity property (Connectivity NWS) and the property of having a spanning star (Stars NWS). Since both problems are NP-hard, we study their parameterized and fine-grained complexity. We provide a tight $2^{\Omega(n^2+c)} poly(n+|Z|)$-time running time lower bound based on the ETH for both problems, where $n$ is the number of vertices in $G$. The lower bound holds even in the restricted case when all communities have size at most 4, $G$ is a clique, and every edge has unit weight. For the connectivity property, the unit weight case with $G$ being a clique is the well-studied problem of computing a hypergraph support with a minimum number of edges. We then study the complexity of both problems parameterized by the feedback edge number $t$ of the solution graph $G'$. For Stars NWS, we present an XP-algorithm for $t$. This answers an open question by Korach and Stern [Disc. Appl. Math. '08] who asked for the existence of polynomial-time algorithms for $t=0$. In contrast, we show for Connectivity NWS that known polynomial-time algorithms for $t=0$ [Korach and Stern, Math. Program. '03; Klemz et al., SWAT '14] cannot be extended by showing that Connectivity NWS is NP-hard for $t=1$

On the complexity of overlaying a hypergraph with a graph with bounded maximum degree

Let G and H be respectively a graph and a hypergraph defined on a same set of vertices, and let F be a graph. We say that G F-overlays a hyperedge S of H if the subgraph of G induced by S contains F as a spanning subgraph, and that G F-overlays H if it F-overlays every hyperedge of H. For a fixed graph F and a fixed integer k, the problem (∆ ≤ k)-F-Overlay consists in deciding whether there exists a graph with maximum degree at most k that F-overlays a given hypergraph H. In this paper, we prove that for any graph F which is neither complete nor anticomplete, there exists an integer np(F) such that (∆ ≤ k)-F-Overlay is NP-complete for all k ≥ np(F)

Problèmes de graphes motivés par des modèles basse et haute résolution de grands assemblages de protéines

To explain the biological function of a molecular assembly (MA), one has to know its structural description. It may be ascribed to two levels of resolution: low resolution (i.e. molecular interactions) and high resolution (i.e. relative position and orientation of each molecular subunit, called conformation). Our thesis aims to address the two problems from graph aspects.The first part focuses on low resolution problem. Assume that the composition (complexes) of a MA is known, we want to determine all interactions ofsubunits in the MA which satisfies some property. It can be modeled as a graph problem by representing a subunit as a vertex, then a subunit interaction is an edge, and a complex is an induced subgraph. In our work, we use the fact that a subunit has a bounded number of interactions. It leads to overlaying graph with bounded maximum degree. For a graph family F and a fixed integer k, given a hypergraph H = (V (H), E(H)) (whose edges are subsets of vertices) and an integer s, M AX (∆ ≤ k)-F -O VERLAY consists in deciding whether there exists a graph with degree at most k such that there are at least s hyperedges in which the subgraph induced by each hyperedge (complex) contains an element of F. When s = |E(H)|, it is called (∆ ≤ k)-F -O VERLAY . We present complexity dichotomy results (P vs. NP-complete) for MAX (∆ ≤ k)-F-OVERLAY and (∆ ≤ k)-F-OVERLAY depending on pairs (F, k).The second part presents our works motivated by high resolution problem. Assume that we are given a graph representing the interactions of subunits, a finite set of conformations for each subunit and a weight function assessing the quality of the contact between two subunits positioned in the assembly. Discrete Optimization of Multiple INteracting Objects (D OMINO ) aims to find conformations for the subunits maximizing a global utility function. We propose a new approach based on this problem in which the weight function is relaxed, CONFLICT COLORING . We present studies from both theoretical and experimental points of view. Regarding the theory, we provide a complexity dichotomy result and also algorithmic methods (approximation and fixed paramater tracktability). Regarding the experiments, we build instances of CONFLICT COLORING associated with Voronoi diagrams in the plane. The obtained statistics provide information on the dependencies of the existences of a solution, to parameters used in ourexperimental setup.Pour comprendre les fonctions biologiques d’un assemblage moléculaire (AM), il est utile d’en avoir une représentation structurale. Celle-ci peut avoir deux niveaux de résolution : basse résolution (i.e. interactions moléculaires) et haute résolution (i.e. position relative et orientation de chaque sous-unité, appelée conformation). Cette thèse s’intéresse à trouver de telles représentations à l’aide de graphes.Dans la première partie, nous cherchons des représentations basse résolution. Etant donné la composition des complexes d’un AM, notre but est de déterminer les interactions entre ses différentes sous-unités. Nous modélisons l’AM à l’aide d’un graphe : les sous-unités sont les sommets, les interactions entre elles sont les arêtes et un complexe est un sous-graphe induit. Utilisant le fait qu’une sous-unité n’a qu’un nombre limité d’interactions, nous arrivons au problème suivant. Pour un graphe F et un entier k fixés, étant donné un hypergraphe H et un entier s, MAX (∆ ≤ k)-F-OVERLAY consiste à décider s’il existe un graphe de degré au plus k tel qu’au moins s hyperarêtes de H induisent un sous-graphe contenant F (en tant que sous-graphe). La restriction au cas s = |E(H)| est appelée (∆ ≤ k)-F-OVERLAY . Nous donnons une dichotomie de complexité (P vs. NP-complet) pour MAX (∆ ≤ k)-F-OVERLAY et (∆ ≤ k)-F-OVERLAY en fonction du couple (F, k).Dans la seconde partie, nous nous attaquons à la haute résolution. Nous sont donnés un graphe représentant les interactions entre sous-unités, un ensemble de conformations possibles pour chaque sous-unité et une fonction de poids représentant la qualité de contact entre les conformations de deux sous-unités interagissant dans l’assemblage. Le problème Discrete Optimization of Multiple INteracting Objects (D OMINO ) consiste alors à trouver les conformations pour les sous-unités qui maximise une fonction d’utilité globale. Nous proposons une nouvelle approche à ce problème en relâchant la fonction de poids, ce qui mène au problème de graphe CONFLICT COLORING . Nous donnons tout d’abord des résultats de complexité et des algorithmes (d’approximation et à paramètre fixé). Nous menons ensuite des expérimentations sur des instances de CONFLICT COLORING associées à des diagrammes de Voronoi dans le plan. Les statistiques obtenues nous informent sur comment les parmètres de notre montage expérimental influe sur l’existence d’une solution