Greedy coloring

About: Greedy coloring is a research topic. Over the lifetime, 2017 publications have been published within this topic receiving 42646 citations. The topic is also known as: sequential coloring.

Using tabu search techniques for graph coloring

Abstract: Tabu search techniques are used for moving step by step towards the minimum value of a function. A tabu list of forbidden movements is updated during the iterations to avoid cycling and being trapped in local minima. Such techniques are adapted to graph coloring problems. We show that they provide almost optimal colorings of graphs having up to 1000 nodes and their efficiency is shown to be significantly superior to the famous simulated annealing.

Some remarks on greedy algorithms

Abstract: Estimates are given for the rate of approximation of a function by means of greedy algorithms. The estimates apply to approximation from an arbitrary dictionary of functions. Three greedy algorithms are discussed: the Pure Greedy Algorithm, an Orthogonal Greedy Algorithm, and a Relaxed Greedy Algorithm.

Hybrid Evolutionary Algorithms for Graph Coloring

Abstract: A recent and very promising approach for combinatorial optimization is to embed local search into the framework of evolutionary algorithms. In this paper, we present such hybrid algorithms for the graph coloring problem. These algorithms combine a new class of highly specialized crossover operators and a well-known tabu search algorithm. Experiments of such a hybrid algorithm are carried out on large DIMACS Challenge benchmark graphs. Results prove very competitive with and even better than those of state-of-the-art algorithms. Analysis of the behavior of the algorithm sheds light on ways to further improvement.

Smallest-last ordering and clustering and graph coloring algorithms

Abstract: Smallest-last vertex ordering and prlonty search are utdlzed to show for any graph G = (IT, E) that the set of all connected subgraphs maxunal with respect to their minimum degree can be determined in O(I EI + I VI) time and 21El + O(I VI) space It is further noted that the smallest-last graph coloring algonthrn can be unplemented in O(I E I + I V[) tune, and particularly effective aspects of the resulting coloring are discussed.

The community-search problem and how to plan a successful cocktail party

Abstract: A lot of research in graph mining has been devoted in the discovery of communities. Most of the work has focused in the scenario where communities need to be discovered with only reference to the input graph. However, for many interesting applications one is interested in finding the community formed by a given set of nodes. In this paper we study a query-dependent variant of the community-detection problem, which we call the community-search problem: given a graph G, and a set of query nodes in the graph, we seek to find a subgraph of G that contains the query nodes and it is densely connected. We motivate a measure of density based on minimum degree and distance constraints, and we develop an optimum greedy algorithm for this measure. We proceed by characterizing a class of monotone constraints and we generalize our algorithm to compute optimum solutions satisfying any set of monotone constraints. Finally we modify the greedy algorithm and we present two heuristic algorithms that find communities of size no greater than a specified upper bound. Our experimental evaluation on real datasets demonstrates the efficiency of the proposed algorithms and the quality of the solutions we obtain.