@MISC{Raffaella_graphalgorithms, author = {Gentilini Raffaella}, title = {Graph Algorithms for Massive Data-Sets}, year = {} }

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Abstract

In this dissertation we face a number of fundamental graph problems which, on the one hand, share applications to the state explosion problem in model checking, and, on the other hand, pose interesting algorithmic questions when very large graphs are dealt with. In more detail, this thesis is divided into three main parts. Part I (Symbolic Graph Algorithms) deals with the algorithmic solution of fundamental graph problems (especially graph connectivity problems) assuming a symbolic OBDD-based graph representation. Working on symbolically represented data has potential: the standards achieved in graph sizes (playing a crucial role, for example, in verification, VLSI design, and CAD) are definitely higher. On the other hand, symbolic algorithmâ€™s design generates constraints as well. For example, Depth First Search is not feasible in the symbolic setting and our approach suggests a symbolic framework to tackle those problems which, in the standard case, are naturally solved by a DFS-based algorithm. Part II (Equivalence Based Graph Reduction) deals with well known bisimulation and simulation graph reductions. In particular, our purpose is to show a fil rouge