Massive Quasi-Clique Detection

  • James Abello
  • Mauricio G. C. Resende
  • Sandra Sudarsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2286)


We describe techniques that are useful for the detection of dense subgraphs (quasi-cliques) in massive sparse graphs whose vertex set, but not the edge set, fits in RAM. The algorithms rely on efficient semi-external memory algorithms used to preprocess the input and on greedy randomized adaptive search procedures (GRASP) to extract the dense subgraphs. A software platform was put together allowing graphs with hundreds of millions of nodes to be processed. Computational results illustrate the effectiveness of the proposed methods.


Local Search Maximum Clique Greedy Randomized Adaptive Search Procedure Edge Density Dense Subgraph 
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 2002

Authors and Affiliations

  • James Abello
    • 1
  • Mauricio G. C. Resende
    • 1
  • Sandra Sudarsky
    • 2
  1. 1.AT&T Labs ResearchFlorham ParkUSA
  2. 2.Siemens Corporate Research, IncPrincetonUSA

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