External-Memory Network Analysis Algorithms for Naturally Sparse Graphs

  • Michael T. Goodrich
  • Paweł Pszona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6942)

Abstract

In this paper, we present a number of network-analysis algorithms in the external-memory model. We focus on methods for large naturally sparse graphs, that is, n-vertex graphs that have O(n) edges and are structured so that this sparsity property holds for any subgraph of such a graph. We give efficient external-memory algorithms for the following problems for such graphs:

  1. 1

    Finding an approximate d-degeneracy ordering.

     
  2. 2

    Finding a cycle of length exactly c.

     
  3. 3

    Enumerating all maximal cliques.

     
Such problems are of interest, for example, in the analysis of social networks, where they are used to study network cohesion.

Keywords

Random Graph Undirected Graph Maximal Clique External Memory Recursive Call 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michael T. Goodrich
    • 1
  • Paweł Pszona
    • 1
  1. 1.Dept. of Computer ScienceUniversity of CaliforniaIrvineUSA

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