Graph searching.
Published: 2005
Language: eng
Number of pages: 115
Description: Graph searching is a simple and widely used technique for exploring the vertices and edges of a graph. A graph search starts by visiting an initial vertex and systematically visits new vertices by iteratively traversing edges incident with a vertex previously visited. The order in which vertices are discovered yields an ordering of the vertices of a graph.We find similar characterizations of vertex orderings for all other major graph search paradigms. These simple characterizations give a unified view of graph searching, and lead to the identification of two new search paradigms, Lexicographic Depth-First Search and Maximal Neighborhood Search.Two well-known forms of graph search are Breadth-First Search and Depth-First Search. Recently, two restricted versions of graph search, Lexicographic Breadth-First Search and Maximum Cardinality Search, have been applied to a wide variety of problems. Many of these results rely on simple characterizations of vertex orderings that these algorithms can compute.To further unify our understanding of graph searching, we introduce a generalized technique called Maximal Label Search to express graph labelling algorithms. We characterize labelling schemes that correspond to each of the major search paradigms using our vertex ordering characterizations. We show that labelling schemes can capture some, but not all, types of graph search on complements of graphs without the need to compute the complement.We illustrate the power of our new characterizations by addressing the problem of finding minimal triangulations of arbitrary graphs. We show that a class of algorithms derived from some of the main graph search paradigms can compute minimal elimination orderings, and show how this generalizes many known results.
Notes: Source: Dissertation Abstracts International, Volume: 66-10, Section: B, page: 5500.

Thesis (Ph.D.)--University of Toronto, 2005.

Electronic version licensed for access by U. of T. users.