About igraph releases and other things

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.

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

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 manual.

igraph includes some functions to convert graphs to and from
`graphNEL`

objects as defined in the graph package
(`igraph.to.graphNEL`

,
`igraph.from.graphNEL`

) and
sparse matrices using the Matrix package
(`get.adjacency`

,
`graph.adjacency`

,
see the `sparse`

argument).

A new function was added to create graphs from adjacency lists
(`graph.adjlist`

),
and `graph.data.frame`

has an argument call `vertices`

, this allows easy
construction of graphs with vertex and edge meta data.

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

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

The R interface now supports different vertex shapes when plotting. See more in the R documentation.

- A new layout generator called DrL.
- Uniform sampling of random connected undirected graphs with a given degree sequence.
- Edge labels are plotted at 1/3 of the edge, this is better if the graph has mutual edges.
- Initial and experimental vertex shape support in
`plot`

. - New function,
`graph.adjlist`

creates igraph graphs from adjacency lists. - Conversion to/from graphNEL graphs, from the
`graph`

R package. - Fastgreedy community detection can utilize edge weights now, this was missing from the R interface.
- The
`arrow.width`

graphical parameter was added. `graph.data.frame`

has a new argument`vertices`

.`graph.adjacency`

and`get.adjacency`

support sparse matrices, the`Matrix`

package is required to use this functionality.`graph.adjacency`

adds column/row names as`name`

attribute.- Weighted shortest paths using Dijkstra’s or the Belmann-Ford algorithm.
- Shortest path functions return
`Inf`

for unreachable vertices. - New function
`is.mutual`

to find mutual edges in a directed graph. - Added inverse log-weighted similarity measure (a.k.a. Adamic/Adar similarity).
`preference.game`

and`asymmetric.preference.game`

were rewritten, they are O(|V|+|E|) now, instead of O(|V|^2).- Edge weight support in function
`get.shortest.paths`

, it uses Dijkstra’s algorithm.

- A bug was corrected in
`write.pajek.bgraph`

. - Several bugs were corrected in
`graph.adjacency`

. - 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 basic igraph constructor (
`igraph_empty_attrs`

, all functions are expected to call this internally) now checks whether the number of vertices is finite. - 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