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Partitioning Random Graphs with General Degree Distributions

  • Amin Coja-Oghlan
  • André Lanka
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 273)

Abstract

We consider the problem of recovering a planted partition (e.g., a small bisection or a large cut) from a random graph. During the last 30 years many algorithms for this problem have been developed that work provably well on models resembling the Erd˝s-Rényi model Gn,m. Since in these random graph models edges are distributed very uniformly, the recent theory of large networks provides convincing evidence that real-world networks, albeit looking random in some sense, cannot sensibly be described by these models. Therefore, a variety of new types of random graphs have been introduced. One of the most popular of these new models is characterized by a prescribed expected degree sequence. We study a natural variant of this model that features a planted partition, the main result being that there is a polynomial time algorithm for recovering (a large share of) the planted partition efficiently. In contrast to prior work, the algorithm’s input only consists of the graph, i.e., no further parameters of the distribution (such as the expected degree sequence) are required.

Keywords

Adjacency Matrix Random Graph Degree Distribution Average Degree Edge Density 
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

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Amin Coja-Oghlan
    • 1
  • André Lanka
    • 2
  1. 1.Department of Mathematical SciencesCarnegie Mellon UniversityPittsburghUSA
  2. 2.Fakultät für InformatikTechnische Universität Chemnitz09107 ChemnitzGermany

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