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Finding Connected Subgraphs of Fixed Minimum Density: Implementation and Experiments

  • Christian Komusiewicz
  • Manuel Sorge
  • Kolja Stahl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9125)

Abstract

We consider the following problem. Given a graph and a rational number \(\mu \), \(0 < \mu \le 1\), find a connected subgraph of density at least \(\mu \) with the largest number of vertices. Here, the density of an \(n\)-vertex graph with \(m\) edges is \(m/\left( {\begin{array}{c}n\\ 2\end{array}}\right) \). This problem arises in many application contexts such as community detection in social networks. We implement a branch and bound algorithm and tune it for efficiency on sparse real-world graphs for the case \(\mu \ge 1/2\). Central issues for the implementation are the choice of branching candidates, two new upper bounding procedures, and several data reduction and early termination rules.

Keywords

Community Detection Input Graph Dense Subgraph Edge Probability Active Vertex 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Christian Komusiewicz
    • 1
  • Manuel Sorge
    • 1
  • Kolja Stahl
    • 1
  1. 1.Institut für Softwaretechnik und Theoretische InformatikBerlinGermany

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