Commit ed939d64 authored by Andreas Tille's avatar Andreas Tille

Imported Upstream version 1.0.1

parent 6f85dc77

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igraph authors, in alphabetical order:
Patrick R. Amestoy AMD library
Adelchi Azzalini igraph.options based on the sm package
Tamas Badics GLPK
Gregory Benison Minimum cut calculation
Adrian Bowman igraph.options based on the sm package
Keith Briggs Parts from the Very Nauty Graph Library
Geometric random graphs
Various patches and bug fixes
Jeroen Bruggeman spinglass community detection
Burt's constraints
Juergen Buchmueller Big number math implementation
Carter T. Butts Some layout algorithms from the SNA R package
bonpow function in the SNA R package
Some R manual pages, from the SNA R package
Aaron Clauset Hierarchical random graphs
J.T. Conklin logbl function
Topher Cooper GSL random number generators (not used in R)
Gabor Csardi Most of igraph
Trevor Croft simpleraytracer
Peter DalGaard zeroin root finder
Timothy A Davis CXSPARSE: a Concise Sparse Matrix package - Extended
AMD library
Sparse matrix column ordering
Laurent Deniau Bits of the error handling system
Ulrich Drepper logbl function
Iain S. Duff AMD library
S.I. Feldman f2c
David Firth Display data frame in Tk, from relimp package
P. Foggia VF2 graph isomorphism algorithm
John Fox R: suppressing X11 warnings
Alan George GLPK
John Gilbert Sparse matrix column ordering
D.Goldfarb GLPK
Brian Gough GSL random number generators (not used in R)
Tom Gregorovic Multilevel community detection
M.Grigoriadis GLPK
Oscar Gustafsson GLPK
Paul Hsieh pstdint.h
Ross Ihaka Some random number generators (not used in R)
Tommi Junttila BLISS graph isomorphism library
Petteri Kaski BLISS graph isomorphism library
Oleg Keselyov zeroin root finder
Darwin Klingman GLPK
Donald E. Knuth GLPK
Stefan I. Larimore Sparse matrix column ordering
Yusin Lee GLPK
Richard Lehoucq ARPACK
Rene Locher R arrow drawing function, from IDPmisc package
J.C. Nash BFGS optimizer
Joseph W-H Liu GLPK
Makoto Matsumoto GSL random number generators (not used in R)
Vincent Matossian Graph laplacian
Line graphs
Peter McMahan Cohesive blocking
Andrew Makhorin GLPK
David Morton de Lachapelle Spectral coarse graining
Laurence Muller Fixes for compilation on MS Visual Studio
Fionn Murtagh Order a hierarchical clustering
Emmanuel Navarro infomap community detection
Various fixes and patches
Tamas Nepusz Most of igraph
Esmond Ng Sparse matrix column ordering
Kevin O'Neill Maximal independent vertex sets
Takuji Nishimura GSL random number generators (not used in R)
Jim Orlin GLPK
Patric Ostergard GLPK
Elliot Paquette psumtree data type
Pascal Pons walktrap community detection
Joerg Reichardt spinglass community detection
Marc Rieffel GSL random number generators (not used in R)
B.D. Ripley igraph.options based on the sm package
BFGS optimizer
Various bug fixes
Martin Rosvall infomap community detection
Andreas Ruckstuhl R arrow drawing function, from IDPmisc package
Heinrich Schuchardt GLPK
J.K. Reid GLPK
C. Sansone VF2 graph isomorphism algorithm
Michael Schmuhl The graphopt layout generator
Christine Solnon LAD graph isomorphism library
Danny Sorensen ARPACK
James Theiler GSL random number generators (not used in R)
Samuel Thiriot Interconnected islands graph generator
Vincent A. Traag spinglass community detection
Magnus Torfason R operators that work by name
Minh Van Nguyen Microscopic update rules
Various test cases
Many documentation and other fixes
M. Vento VF2 graph isomorphism algorithm
Fabien Viger gengraph graph generator
Phuong Vu ARPACK
P.J. Weinberger f2c
Garrett A. Wollman qsort
B.N. Wylie DrL layout generator
Chao Yang ARPACK
Institutional copyright owners:
Free Software Foundation, Inc Code generated by bison
Sandia Corporation DrL layout generator
The R Development Core Team Some random number generators (not used in R)
R: as.dendrogram from stats package
The Regents of the University of California qsort
Xerox PARC Sparse matrix column ordering
Other contributors
Neal Becker Patches to compile with gcc 4.4
Richard Bowman R patches
Alex Chen Patch to compile on Intel compilers
Daniel Cordeiro Patches
Tom Gregorovic Bug fixes
Mayank Lahiri Forest fire game fix
John Lapeyre Patches
Christopher Lu Various fixes and patches
André Panisson R patches
Bob Pap Bug fixes
Keith Ponting R package bug fixes
Martin J Reed Bug fixes
Elena Tea Russo Bug fixes
KennyTM Bug fixes
Jordi Torrents Patches
Matthew Walker Various patches
Kai Willadsen Arrow size support in Python
Package: igraph
Version: 0.7.1
Date: 2014-04-22
Title: Network analysis and visualization
Version: 1.0.1
Title: Network Analysis and Visualization
Author: See AUTHORS file.
Maintainer: Gabor Csardi <>
Description: Routines for simple graphs and network analysis. igraph can
handle large graphs very well and provides functions for generating random
and regular graphs, graph visualization, centrality indices and much more.
Description: Routines for simple graphs and network analysis. It can
handle large graphs very well and provides functions for generating random
and regular graphs, graph visualization, centrality methods and much more.
Depends: methods
Imports: Matrix
Suggests: igraphdata, stats4, rgl, tcltk, graph, ape
Imports: Matrix, magrittr, NMF, irlba
Suggests: igraphdata, stats4, rgl, tcltk, graph, ape, scales
License: GPL (>= 2)
SystemRequirements: gmp, libxml2
Packaged: 2014-04-22 18:00:26 UTC; gaborcsardi
Collate: 'adjacency.R' 'auto.R' 'assortativity.R' 'attributes.R'
'basic.R' 'bipartite.R' 'centrality.R' 'centralization.R'
'cliques.R' 'cocitation.R' 'cohesive.blocks.R' 'printr.R'
'community.R' 'components.R' 'console.R' 'conversion.R'
'data_frame.R' 'decomposition.R' 'degseq.R' 'demo.R'
'embedding.R' 'epi.R' 'fit.R' 'flow.R' 'foreign.R' 'games.R'
'glet.R' 'hrg.R' 'igraph-package.R' 'incidence.R' 'indexing.R'
'interface.R' 'iterators.R' 'layout.R' 'layout_drl.R'
'lazyeval.R' 'make.R' 'mgclust.R' 'minimum.spanning.tree.R'
'motifs.R' 'nexus.R' 'operators.R' 'other.R' 'package.R'
'palette.R' 'par.R' 'paths.R' 'plot.R' 'plot.common.R'
'plot.shapes.R' 'pp.R' 'print.R' 'random_walk.R' 'rewire.R'
'scan.R' 'scg.R' 'sgm.R' 'similarity.R' 'simple.R' 'sir.R'
'socnet.R' 'sparsedf.R' ''
'' 'test.R' 'tkplot.R' 'topology.R'
'triangles.R' 'utils.R' 'uuid.R' 'versions.R' 'weakref.R'
NeedsCompilation: yes
Packaged: 2015-06-26 01:04:44 UTC; gaborcsardi
Repository: CRAN
Date/Publication: 2014-04-22 23:08:29
Date/Publication: 2015-06-26 11:13:24
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## -----------------------------------------------------------------------
## IGraph R package
## Copyright (C) 2015 Gabor Csardi <>
## 334 Harvard street, Cambridge, MA 02139 USA
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## GNU General Public License for more details.
## You should have received a copy of the GNU General Public License
## along with this program; if not, write to the Free Software
## Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
## 02110-1301 USA
## -----------------------------------------------------------------------
#' Assortativity coefficient
#' The assortativity coefficient is positive is similar vertices (based on some
#' external property) tend to connect to each, and negative otherwise.
#' The assortativity coefficient measures the level of homophyly of the graph,
#' based on some vertex labeling or values assigned to vertices. If the
#' coefficient is high, that means that connected vertices tend to have the
#' same labels or similar assigned values.
#' M.E.J. Newman defined two kinds of assortativity coefficients, the first one
#' is for categorical labels of vertices. \code{assortativity_nominal}
#' calculates this measure. It is defines as
#' \deqn{r=\frac{\sum_i e_{ii}-\sum_i a_i b_i}{1-\sum_i a_i b_i}}{
#' r=(sum(e(i,i), i) - sum(a(i)b(i), i)) / (1 - sum(a(i)b(i), i))}
#' where \eqn{e_{ij}}{e(i,j)} is the fraction of edges connecting vertices of
#' type \eqn{i} and \eqn{j}, \eqn{a_i=\sum_j e_{ij}}{a(i)=sum(e(i,j), j)} and
#' \eqn{b_j=\sum_i e_{ij}}{b(j)=sum(e(i,j), i)}.
#' The second assortativity variant is based on values assigned to the
#' vertices. \code{assortativity} calculates this measure. It is defined as
#' \deqn{r=\frac1{\sigma_q^2}\sum_{jk} jk(e_{jk}-q_j q_k)}{
#' sum(jk(e(j,k)-q(j)q(k)), j, k) / sigma(q)^2}
#' for undirected graphs (\eqn{q_i=\sum_j e_{ij}}{q(i)=sum(e(i,j), j)}) and as
#' \deqn{r=\frac1{\sigma_o\sigma_i}\sum_{jk}jk(e_{jk}-q_j^o q_k^i)}{
#' sum(jk(e(j,k)-qout(j)qin(k)), j, k) / sigma(qin) / sigma(qout) }
#' for directed ones. Here \eqn{q_i^o=\sum_j e_{ij}}{qout(i)=sum(e(i,j), j)},
#' \eqn{q_i^i=\sum_j e_{ji}}{qin(i)=sum(e(j,i), j)}, moreover,
#' \eqn{\sigma_q}{sigma(q)}, \eqn{sigma_o}{sigma(qout)} and
#' \eqn{sigma_i}{sigma(qin)} are the standard deviations of \eqn{q},
#' \eqn{q^o}{qout} and \eqn{q^i}{qin}, respectively.
#' The reason of the difference is that in directed networks the relationship
#' is not symmetric, so it is possible to assign different values to the
#' outgoing and the incoming end of the edges.
#' \code{assortativity_degree} uses vertex degree (minus one) as vertex values
#' and calls \code{assortativity}.
#' @aliases assortativity assortativity_degree
#' assortativity.nominal assortativity_nominal
#' @param graph The input graph, it can be directed or undirected.
#' @param types Vector giving the vertex types. They as assumed to be integer
#' numbers, starting with one. Non-integer values are converted to integers
#' with \code{\link{as.integer}}.
#' @param types1 The vertex values, these can be arbitrary numeric values.
#' @param types2 A second value vector to be using for the incoming edges when
#' calculating assortativity for a directed graph. Supply \code{NULL} here if
#' you want to use the same values for outgoing and incoming edges. This
#' argument is ignored (with a warning) if it is not \code{NULL} and undirected
#' assortativity coefficient is being calculated.
#' @param directed Logical scalar, whether to consider edge directions for
#' directed graphs. This argument is ignored for undirected graphs. Supply
#' \code{TRUE} here to do the natural thing, i.e. use directed version of the
#' measure for directed graphs and the undirected version for undirected
#' graphs.
#' @return A single real number.
#' @author Gabor Csardi \email{}
#' @references M. E. J. Newman: Mixing patterns in networks, \emph{Phys. Rev.
#' E} 67, 026126 (2003) \url{}
#' M. E. J. Newman: Assortative mixing in networks, \emph{Phys. Rev. Lett.} 89,
#' 208701 (2002) \url{}
#' @keywords graphs
#' @examples
#' # random network, close to zero
#' assortativity_degree(sample_gnp(10000, 3/10000))
#' # BA model, tends to be dissortative
#' assortativity_degree(sample_pa(10000, m=4))
#' @include auto.R
assortativity <- assortativity
#' @rdname assortativity
assortativity_nominal <- assortativity_nominal
#' @rdname assortativity
assortativity_degree <- assortativity_degree
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