About igraph releases and other things

python-igraph 0.5.1

igraph 0.5.1 Release Notes

igraph 0.5.1 is a bugfix release, but it actually contains many important new things as well. Here is a brief summary about each of them. See below for the complete list of changes.

The DrL layout generator was added

This is a sophisticated and efficient layout generator written by Shawn Martin and colleagues. See more in the Python documentation.

Uniform sampling of random graphs with given degree sequence

A nice random graph generator that conditions on the degree of vertices was added. It can generate undirected connected graphs. The algorithm and the implementation was done by Fabien Viger and Matthieu Latapy. See more in the Python documentation.

Weighted shortest path algorithms

Both the Dijkstra and the Belmann-Ford algorithms were added. See more in the Python manual.

Function to test edge reciprocity

Mutuality can be tested for each edge now. See more in the Python manual.

New in the Python interface

  • A new layout generator called DrL.
  • Uniform sampling of random connected undirected graphs with a given degree sequence.
  • Methods parameters accepting igraph.IN, igraph.OUT and igraph.ALL constants now also accept these as strings ("in", "out" and "all"). Prefix matches also allowed as long as the prefix match is unique.
  • Graph.shortest_paths() now supports edge weights (Dijkstra’s and Bellman-Ford algorithm implemented)
  • Graph.get_shortest_paths() also supports edge weights (only Dijkstra’s algorithm yet)
  • Added Graph.is_mutual() to find mutual edges in a directed graph.
  • Added inverse log-weighted similarity measure (a.k.a. Adamic/Adar similarity).
  • and were rewritten, they are O(|V|+|E|) now, instead of O(|V|^2).
  • ARPACK options can now be modified from the Python interface (thanks to Kurt Jacobson)
  • Layout.to_radial() added – now you can create a top-down tree layout by the Reingold-Tilford algorithm and then turn it to a radial tree layout
  • Added Graph.write_pajek() to save graphs in Pajek format
  • Some vertex and edge related methods can now also be accessed via the methods of VertexSeq and EdgeSeq, restricted to the current vertex/edge sequence of course
  • Visualisations now support triangle shaped vertices
  • Added Graph.mincut()
  • Added Graph.Weighted_Adjacency() to create graphs from weighted adjacency matrices
  • Kamada-Kawai and Fruchterman-Reingold layouts now accept initial vertex positions
  • Graph.Preference() and Graph.Asymmetric_Preference() were rewritten, they are O(|V|+|E|) now, instead of O(|V|^2).

Bugs corrected in the Python interface

  • Graph.constraint() now properly returns floats instead of integers (thanks to Eytan Bakshy)
  • Graphs given by adjacency matrices are now finally loaded and saved properly
  • Graph.Preference() now accepts floats in type distributions
  • A small bug in Graph.community_edge_betweenness() corrected
  • Some bugs in numeric attribute handling resolved
  • VertexSeq and EdgeSeq objects can now be subsetted by lists and tuples as well
  • Fixed a bug when dealing with extremely small layout sizes
  • Eigenvector centality now always return positive values
  • Graph.authority_score() now really returns the authority scores instead of the hub scores (blame copypasting)
  • Pajek reader bug corrected, used to segfault if *Vertices was missing.
  • Directedness is handled correctly when writing GML files. (But note that ‘correct’ conflicts the standard here.)
  • Corrected a bug when calculating weighted, directed PageRank on an undirected graph. (Which does not make sense anyway.)
  • Several bugs were fixed in the Reingold-Tilford layout to avoid edge crossings.
  • A bug was fixed in the GraphML reader, when the value of a graph attribute was not specified.
  • Fixed a bug in the graph isomorphism routine for small (3-4 vertices) graphs.
  • Corrected the random sampling implementation (igraph_random_sample), now it always generates unique numbers. This affects the G(n,m) Erdos-Renyi generator, it always generates simple graphs now.
  • The LGL, NCOL and Pajek graph readers handle errors properly now.
  • The non-symmetric ARPACK solver returns results in a consistent form now.
  • The fast greedy community detection routine now checks that the graph is simple.
  • The LGL and NCOL parsers were corrected to work with all kinds of end-of-line encodings.
  • Hub & authority score calculations initialize ARPACK parameters now.
  • Fixed a bug in the Walktrap community detection routine, when applied to unconnected graphs.
  • Several small memory leaks were removed, and a big one from the Spinglass community structure detection function