Displaying similar documents to “Computing communities in large networks using random walks.”

Comparison of algorithms in graph partitioning

Alain Guénoche (2008)

RAIRO - Operations Research - Recherche Opérationnelle

Similarity:

We first describe four recent methods to cluster vertices of an undirected non weighted connected graph. They are all based on very different principles. The fifth is a combination of classical ideas in optimization applied to graph partitioning. We compare these methods according to their ability to recover classes initially introduced in random graphs with more edges within the classes than between them.

Solving maximum independent set by asynchronous distributed hopfield-type neural networks

Giuliano Grossi, Massimo Marchi, Roberto Posenato (2006)

RAIRO - Theoretical Informatics and Applications

Similarity:

We propose a heuristic for solving the maximum independent set problem for a set of processors in a network with arbitrary topology. We assume an asynchronous model of computation and we use modified Hopfield neural networks to find high quality solutions. We analyze the algorithm in terms of the number of rounds necessary to find admissible solutions both in the worst case (theoretical analysis) and in the average case (experimental Analysis). We show that our heuristic is better...

Heuristic and metaheuristic methods for computing graph treewidth

François Clautiaux, Aziz Moukrim, Stéphane Nègre, Jacques Carlier (2004)

RAIRO - Operations Research - Recherche Opérationnelle

Similarity:

The notion of treewidth is of considerable interest in relation to NP-hard problems. Indeed, several studies have shown that the tree-decomposition method can be used to solve many basic optimization problems in polynomial time when treewidth is bounded, even if, for arbitrary graphs, computing the treewidth is NP-hard. Several papers present heuristics with computational experiments. For many graphs the discrepancy between the heuristic results and the best lower bounds is still very...