251. Non-Searchability of Random Scale-Free Graphs
- Author
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Philippe Duchon, Nicole Eggemann, Nicolas Hanusse, Coudert, David, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Algorithmics for computationally intensive applications over wide scale distributed platforms (CEPAGE), Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Department of Mathematical Sciences [Brunel], Brunel University London [Uxbridge], ANR-05-MMSA-0006,ALPAGE,ALgorithmique des Plates-formes A Grande Echelle(2005), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest
- Subjects
Block graph ,Random graph ,[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,Computer science ,[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS] ,Voltage graph ,[INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS] ,0102 computer and information sciences ,16. Peace & justice ,Degree distribution ,01 natural sciences ,1-planar graph ,Vertex (geometry) ,Combinatorics ,[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,010104 statistics & probability ,Indifference graph ,010201 computation theory & mathematics ,0101 mathematics ,ComputingMilieux_MISCELLANEOUS ,MathematicsofComputing_DISCRETEMATHEMATICS ,Hopcroft–Karp algorithm - Abstract
In this paper, we investigate the efficiency of local search algorithms for some relatively unstructured networks. By local search, we mean that each vertex has a knowledge of its very close neighborhood, typically the identities, degrees and possibly the contents. Assuming there is no preprocessing step, we are interested in seeking a vertex of a given identity starting from any vertex. The time complexity of search is expressed as the number of vertices to explore before reaching the target or a neighbor of the target. For an arbitrary graph and a worst-case labelling of vertices, time complexity can be of the same order as the size of the graph itself. However, there is some hope that for large classes of graphs, the size of the exploration sequence would drastically decrease. Recent literature exhibits non trivial properties shared by the topologies of numerous real-world distributed networks: the combination of a low diameter, a specific degree distribution of the vertices and a sparse network are typically representative of a small-world topology. On the other hand, in a seminal paper, Kleinberg [Kle00] focuses on the ability of a user or a mobile agent to route along shorts paths within a class of random small-world graphs, the navigable small-worlds† using a greedy distributed algorithm (distances within a spanning subgraph are known or can be computed locally). Under some conditions, Kleinberg also proposes polynomial lower bounds valid for any distributed algorithm using the same knowledge of the network even if the diameter is polylogarithmic. However, Kleinberg’s model lacks an important property observed in real networks which are often scalefree. The degree distribution of the vertices tends to follow a power-law distribution, that is the number of vertices of degree δ is proportional to n ( 1 δ )k for an n-vertex graph and k a constant strictly greater than one. Moreover, it has been observed that for real-life networks, k tends to belong to the range [2,3]. For graphs built using Kleinberg’s model, the degree distribution is very different (close to a Poisson distribution). All local‡ distributed search algorithms presented in the literature work as follows: given the set of visited vertices, one should choose the next vertex to explore. The process stops as soon as the target is reached. In this paper, the underlying network is a random scale-free labeled graph whose node identities belong to the range [1,n]. Each vertex knows its own identity and its degree and we assume two models of local knowledge: some information about the neighbors can be known (strong model) or not (weak model). The first model is the more realistic one but our starting point is the second model. Note that Kleinberg’s model assumes more information than our strong model. Up to now, it is still an open problem to know if scale-free graphs are navigable in the polylogarithmic sense. In this paper, we answer negatively to this question for some random scale-free graphs built using a
- Published
- 2007
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