1. 19 Nov, 2017 1 commit
  2. 29 Sep, 2017 1 commit
  3. 01 Jan, 2017 1 commit
  4. 02 Dec, 2016 1 commit
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  6. 17 Sep, 2015 1 commit
  7. 15 Jul, 2015 1 commit
  8. 29 Jan, 2015 1 commit
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  10. 02 Jan, 2014 1 commit
  11. 10 Nov, 2013 1 commit
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  14. 06 Jan, 2013 1 commit
  15. 26 Dec, 2012 1 commit
  16. 02 Aug, 2012 1 commit
  17. 02 May, 2012 1 commit
  18. 26 Aug, 2011 1 commit
  19. 24 Aug, 2011 1 commit
    • Tiago Peixoto's avatar
      Several improvements to random_rewire() / random_graph() · 8240714e
      Tiago Peixoto authored
      This introduces several simplifications and corrections to the graph
      rewire algorithm, to guarantee unbiased sampling.
      
      Now a move is outright rejected if it produces a
      self-loop/parallel-edge, instead of retried. This also adds a
      "non-sweep" mode, where edges are rewired randomly, possibly with
      repetition.
      
      The edge moves are now simplified to the target of the edges only,
      since swaping sources is redundant.
      
      The number of iterations can now be explicitly modified, so it is not
      necessary to call the function more than once, and it is emphasized in
      the documentation that only after sufficiently many iterations can the
      graph be guaranteed to be fully mixed.
      8240714e
  20. 10 Feb, 2011 1 commit
  21. 13 Nov, 2010 1 commit
  22. 25 Jul, 2010 1 commit
  23. 03 May, 2010 1 commit
  24. 07 Mar, 2010 1 commit
  25. 20 Feb, 2010 1 commit
    • Tiago Peixoto's avatar
      Refactor random_graph() · 1f28345e
      Tiago Peixoto authored
      Now the degree sequence is verified with Erdös-Gallai inequalities, and
      the edges are connected in a deterministic fashion. The edges then are
      rewired with the random_rewire() function.
      1f28345e
  26. 06 Sep, 2009 1 commit
  27. 04 Aug, 2009 1 commit
  28. 15 Jul, 2009 1 commit
    • Tiago Peixoto's avatar
      Improve correlated graph generation · e1dd1665
      Tiago Peixoto authored
      random_graph() now uses a modified algorithm for generation of
      correlated graphs, which is more efficient. Instead of giving a function
      which returns a sample of the correlated target degree, the user must
      give a function which will just compute its probability. This
      probability will then be used to choose the edges.
      e1dd1665
  29. 23 May, 2009 1 commit
  30. 10 Mar, 2009 1 commit
    • Tiago Peixoto's avatar
      Implement optional wrapping of graphs to deal with edge index housekeeping · 684efca7
      Tiago Peixoto authored
      Thins changes the graph filtering code slightly to wrap graph types with
      GraphWrap, which automatically updates the edge index list when edges
      are removed and added to the graph.
      
      This also changes how graphs are passed to algorithms, which is now by
      reference instead of pointer. (hence this touches lots of code, but
      changes are trivial)
      684efca7
  31. 27 Jul, 2008 1 commit
  32. 06 Apr, 2008 1 commit