About igraph releases and other things
This is another bug fix release, with a couple of new features. Here are the important ones. See at the end for the complete list of changes.
Bipartite graphs have two kinds of vertices and edges are only allowed to connect opposite kinds. Think of the Hollywood movie graph with actors and movies. igraph 0.5.2 now contains some functions to deal with these kind of networks.
This is a simple and intuitive community finding algorithm, published by Raghavan et al. in 2007 (see docs for full citation). It works by assigning labels to the vertices and then updating the labels based on majority voting among the neighbors.
The DrL layout generator was extended to generate three dimensional layouts. Albeit slower than the regular 2D version, this is a nice addition for those who do visualization in 3D.
A minimal, supported GUI is included now in the R package. It contains
only a small fraction of igraph capabilities, but can be still useful,
e.g. in teaching. You can start it by typing
after loading the igraph package of course.
Johnson’s algorithm is a good choice for finding all shortest paths in a network that has some negative edge weights, but no negative cycles.
A new function was added to calculate the average degree of the neighbors of all or some vertices. It supports the edge weighted version of the measure as well.
tkplot() supports curved
?igraph.plotting for more details.
Apart from the bug fixes, some functions were rewritten to speed them up.
shortest.paths()has now an
algorithmargument to choose from the various implementations manually
cohesive.blocks()now has a
cutsetHeuristicargument to choose between two cutset algorithms
tkplot()arguments to change the drawing area
layout.drl()has a three dimensional mode now.
get.adjacency()and the graph was named
degree.sequence.game()checks now that the sum of the degrees is even
cohesive.blocks()creates its temporary file in the session temporary directory
read.graph()now give error messages when unknown arguments are given
ncvARPACK parameter for
unconnectedwas wrong for unconnected and undirected graphs
graph.adjacency()when the matrix is a sparse matrix of class