VisComms.VisComms History
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This MATLAB code requires that your adjacency matrix (or matrix that shows the connections between people) is sparse. To create a sparse matrix in MATLAB, take your matrix (suppose it's called "A"), type "sparse(A)". This code also requires that you download the package from [[http://www.mathworks.com/matlabcentral/fileexchange/10922  this website]]. (If you are using a Mac with 64bit OS X (version 2009b or later), then instead download the package from [[http://dgleich.wordpress.com/2010/07/08/matlabbglosx64bit/  this website]].
to:
This MATLAB code requires that your adjacency matrix (or matrix that shows the connections between people) is sparse. To create a sparse matrix in MATLAB, take your matrix (suppose it's called "A"), type "sparse(A)". This code also requires that you download the package from [[http://www.mathworks.com/matlabcentral/fileexchange/10922  this website]]. If you are using a Mac with 64bit OS X (version 2009b or later), then instead download the package from [[http://dgleich.wordpress.com/2010/07/08/matlabbglosx64bit/  this website]].
Changed line 11 from:
This MATLAB code requires that your adjacency matrix (or matrix that shows the connections between people) is sparse. To create a sparse matrix in MATLAB, take your matrix (suppose it's called "A"), type "sparse(A)". This code also requires that you download the package from [[http://www.mathworks.com/matlabcentral/fileexchange/10922  this website]]. (If you are using a Mac with 64bit OS X, then instead download the package from [[http://dgleich.wordpress.com/2010/07/08/matlabbglosx64bit/  this website]].
to:
This MATLAB code requires that your adjacency matrix (or matrix that shows the connections between people) is sparse. To create a sparse matrix in MATLAB, take your matrix (suppose it's called "A"), type "sparse(A)". This code also requires that you download the package from [[http://www.mathworks.com/matlabcentral/fileexchange/10922  this website]]. (If you are using a Mac with 64bit OS X (version 2009b or later), then instead download the package from [[http://dgleich.wordpress.com/2010/07/08/matlabbglosx64bit/  this website]].
Changed line 11 from:
This MATLAB code requires that your adjacency matrix (or matrix that shows the connections between people) is sparse. To create a sparse matrix in MATLAB, take your matrix (suppose it's called "A"), type "sparse(A)". This code also requires that you download the package from [[http://www.mathworks.com/matlabcentral/fileexchange/10922  this website]].
to:
This MATLAB code requires that your adjacency matrix (or matrix that shows the connections between people) is sparse. To create a sparse matrix in MATLAB, take your matrix (suppose it's called "A"), type "sparse(A)". This code also requires that you download the package from [[http://www.mathworks.com/matlabcentral/fileexchange/10922  this website]]. (If you are using a Mac with 64bit OS X, then instead download the package from [[http://dgleich.wordpress.com/2010/07/08/matlabbglosx64bit/  this website]].
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 7. drawForceCPie (Draws pie charts for each community) [[(Attach:)drawForceCPie.m]]
*Inputs: **A: the adjacency matrix **XY: the community coordinates, the XY output of all of the above programs that take community structure into consideration. **scores: Categorical data for each node, this is how the colors are chosen. **gn: group numbers found in community detection
*Outputs: **Plot of network, no other outputs.
*Calling this Function: **drawForceCPie(A,XY,scores,gn)
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The visualization code on this page is featured in Amanda L. Traud, Christina Frost, Peter J. Mucha, and Mason A. Porter. “Visualization of communities in networks.” Chaos: An Interdisciplinary Journal of Nonlinear Science 19, no. 4: 041104 (2009).
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The visualization code on this page is featured in "Visualization of communities in networks," Amanda L. Traud, Christina Frost, Peter J. Mucha, and Mason A. Porter, ''Chaos'' '''19''', 041104 (2009).
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Graphs" while respecting individual communities. It uses the FruchtermanReingold algorithm to place the nodes within each community.
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Graphs" while respecting individual communities. It uses the FruchtermanReingold algorithm to place the nodes within each community.
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3. fruc_reinC (Fruchterman and Reingold placement of communities and then Fruchterman and Reingold placement of nodes within communities) [[(Attach:)fruc_reinC.m]]
This function uses the FruchtermanReingold Algorithm to find the optimal
to:
3. fruc_reinC (Fruchterman and Reingold placement of communities and then FruchtermanReingold placement of nodes within communities) [[(Attach:)fruc_reinC.m]]
This function uses the FruchtermanReingold algorithm to find the optimal
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The visualization code on this page is featured in Amanda L. Traud, Christina Frost, Peter J. Mucha, and Mason A. Porter. “Visualization of communities in networks.” Chaos: An Interdisciplinary Journal of Nonlinear Science 19, no. 4: 041104 (2009).
to:
The visualization code on this page is featured in Amanda L. Traud, Christina Frost, Peter J. Mucha, and Mason A. Porter. “Visualization of communities in networks.” Chaos: An Interdisciplinary Journal of Nonlinear Science 19, no. 4: 041104 (2009).
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The visualization code on this page is featured in the following publication:
*[[(Attach:)nonlinear.pdf  One Page Paper for Chaos]] (preprint version)
*Its associated winning [[(Attach:)Poster.pdf  poster]] from the Gallery of Nonlinear Image at the 2009 APS March Meeting
to:
The visualization code on this page is featured in Amanda L. Traud, Christina Frost, Peter J. Mucha, and Mason A. Porter. “Visualization of communities in networks.” Chaos: An Interdisciplinary Journal of Nonlinear Science 19, no. 4: 041104 (2009). *[[http://www.amath.unc.edu/Faculty/mucha/Reprints/ChaosVisComms.pdfArticle (pdf)]] *The associated winning [[(Attach:)Poster.pdfposter (pdf)]] from the Gallery of Nonlinear Images at the 2009 APS March Meeting
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Each of the programs below gives xy coordinates as one of the outputs; these can be put into the following program to produce the graphs that are featured in our Nonlinear Science Gallery poster and paper: [[(Attach:)graphplot2D.m]]
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Each of these programs has xy coordinates as one of the outputs which can be put into the following program to produce the graphs that are featured in our Nonlinear Science Gallery poster and paper: [[(Attach:)graphplot2D.m]]
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#FRKK (FruchtermanReingold placement of communities, KamadaKawai placement of nodes within each community) [[(Attach:)FRKK.m]]
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1. FRKK (FruchtermanReingold placement of communities, KamadaKawai placement of nodes within each community) [[(Attach:)FRKK.m]]
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#KKFR (KamadaKawai placement of communities, FruchtermanReingold placement of nodes within communities) [[(Attach:)KKFR.m]]
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2. KKFR (KamadaKawai placement of communities, FruchtermanReingold placement of nodes within communities) [[(Attach:)KKFR.m]]
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#fruc_reinC (Fruchterman and Reingold placement of communities and then Fruchterman and Reingold placement of nodes within communities) [[(Attach:)fruc_reinC.m]]
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3. fruc_reinC (Fruchterman and Reingold placement of communities and then Fruchterman and Reingold placement of nodes within communities) [[(Attach:)fruc_reinC.m]]
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#KamadaC (KamadaKawai placement of communities and then KamadaKawai placement of nodes within communities) [[(Attach:)KamadaC.m]]
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4. KamadaC (KamadaKawai placement of communities and then KamadaKawai placement of nodes within communities) [[(Attach:)KamadaC.m]]
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#Kamada (KamadaKawai placement of nodes, ignoring community structure) [[(Attach:)Kamada.m]]
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5. Kamada (KamadaKawai placement of nodes, ignoring community structure) [[(Attach:)Kamada.m]]
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#fruc_rein (Fruchterman and Reingold placement of nodes, ignoring community structure) [[(Attach:)fruc_rein.m]]
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6. fruc_rein (Fruchterman and Reingold placement of nodes, ignoring community structure) [[(Attach:)fruc_rein.m]]
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**xy: the matrix of xy coordinates for each node in your network; the first column is the x coordinates and the second column is the y
coordinates.
to:
**xy: the matrix of xy coordinates for each node in your network; the first column gives the x coordinates and the second column gives the y coordinates.
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**factor2: the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases but you may
want to change it based on your data.
to:
**factor2: the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases, but you may want to change it based on your data.
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**factor2: the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community
it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases but you may
to:
**factor2: the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases but you may
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This MATLAB code requires that your adjacency matrix (or matrix that shows the connections between people) is sparse. To create a sparse matrix in MATLAB, take your matrix (suppose it's called "A") "type sparse(A)". This code also requires that you download the package from [[http://www.mathworks.com/matlabcentral/fileexchange/10922  this website]].
to:
This MATLAB code requires that your adjacency matrix (or matrix that shows the connections between people) is sparse. To create a sparse matrix in MATLAB, take your matrix (suppose it's called "A"), type "sparse(A)". This code also requires that you download the package from [[http://www.mathworks.com/matlabcentral/fileexchange/10922  this website]].
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**gn: a list of group numbers for identifying each node by the community in which they they belong. For example, if you have 20 nodes, gn should be a 20 x 1
matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2. You may use any community detection program you see fit.
to:
**gn: a list of group numbers for identifying each node by the community in which they they belong. For example, if you have 20 nodes, gn should be a 20 x 1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2. You may use any community detection program you see fit.
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**factor2: the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases, but you may
want to change it based on your data.
to:
**factor2: the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases, but you may want to change it based on your data.
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**epsilon: the accuracy at which the algorithm acts, .01 works well.
to:
**epsilon: the accuracy at which the algorithm acts; .01 works well.
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**factor2: the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community
it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases, but you may
to:
**factor2: the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases, but you may
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**gn: a list of group numbers for identifying each node by the community
they belong in. For example, if you have 20 nodes, gn should be a 20 x 1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2.
You may use any community detection program you see fit. **epsilon: determines how precise you want to be in your node placement.
We suggest epsilon being .01
**seed: the seed for the random number generator for the initial
placement of the communities
to:
**gn: a list of group numbers for identifying each node by the community in which they they belong. For example, if you have 20 nodes, gn should be a 20 x 1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2. You may use any community detection program you see fit. **epsilon: determines how precise you want to be in your node placement. We suggest epsilon = .01. **seed: the seed for the random number generator for the initial placement of the communities
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**gn: a vector of group numbers, i.e. every node that has the number 1
is in group 1. **epsilon: the accuracy at which you would like the algorithm to act,
.01 tends to work well. **seed: the number used in the random number generator for placing the
communities, any four digit number will work. **factor1: the number to multiply the community coordinates by, 2 tends to
work well, but you may want the communities farther apart. **factor2: the number to multiply the node coordinates by within the
community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a
factor to redistribute them. 4 tends to work in most cases but you may
to:
**gn: a vector of group numbers, i.e. every node that has the number 1 is in group 1. **epsilon: the accuracy at which you would like the algorithm to act; .01 tends to work well. **seed: the number used in the random number generator for placing the communities, any four digit number will work. **factor1: the number to multiply the community coordinates by, 2 tends to work well, but you may want the communities farther apart. **factor2: the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases but you may
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**xynew: the vectors of the xy coordinates for each node in your network, this
is one of the inputs for graphplot2d.m **XY: the vectors of xy coordinates for the communties, this is one of
the inputs for drawForceCPie.m **xyc: the cellarray of xy coordinates for the nodes within each community
that are centered at zero.
to:
**xynew: the vectors of the xy coordinates for each node in your network, this is one of the inputs for graphplot2d.m **XY: the vectors of xy coordinates for the communties, this is one of the inputs for drawForceCPie.m **xyc: the cellarray of xy coordinates for the nodes within each community that are centered at zero.
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**gn: a list of group numbers for identifying each node by the community
they belong in. For example, if you have 20 nodes, gn should be a 20 x 1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2.
You can use any community detection program you see fit. **epsilon: determines how precise you want to be in your node placement.
We suggest epsilon = .01
to:
**gn: a list of group numbers for identifying each node by the community they belong in. For example, if you have 20 nodes, gn should be a 20 x 1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2. You can use any community detection program you see fit. **epsilon: determines how precise you want to be in your node placement. We suggest epsilon = .01.
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**eps: is the accuracy at which the algorithm will act; .01 works well in
most cases. **seed: the seed for the random number generator for initial placement of
the nodes.
to:
**eps: is the accuracy at which the algorithm will act; .01 works well in most cases. **seed: the seed for the random number generator for initial placement of the nodes.
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**xy: the matrix of xy coordinates for each node in your network, the first column is the x coordinates and the second column is the y
to:
**xy: the matrix of xy coordinates for each node in your network; the first column is the x coordinates and the second column is the y
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**gn: a vector of group numbers, i.e. every node that has the number 1
is in group 1. **epsilon: the accuracy at which you would like the algorithm to act,
.01 tends to work well. **seed: the number used in the random number generator for placing the
communities, any four digit number will work. **factor1: the number to multiply the community coordinates by, 2 tends to
work well, but you may want the communities farther apart. **factor2: the number to multiply the node coordinates by within the
community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a
factor to redistribute them. 4 tends to work in most cases but you may
to:
**gn: a vector of group numbers, i.e. every node that has the number 1 is in group 1. **epsilon: the accuracy at which you would like the algorithm to act; .01 tends to work well. **seed: the number used in the random number generator for placing the communities; any 4digit number will work. **factor1: the number to multiply the community coordinates by, 2 tends to work well, but you may want the communities farther apart. **factor2: the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases, but you may
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**xynew: the vectors of the xy coordinates for each node in your network, this
is one of the inputs for graphplot2d.m **XY: the vectors of xy coordinates for the communties, this is one of
the inputs for drawForceCPie.m **xyc: the cellarray of xy coordinates for the nodes within each community
that are centered at zero.
to:
**xynew: the vectors of the xy coordinates for each node in your network, this is one of the inputs for graphplot2d.m **XY: the vectors of xy coordinates for the communties, this is one of the inputs for drawForceCPie.m **xyc: the cellarray of xy coordinates for the nodes within each community that are centered at zero.
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*Inputs:
**A: the adjacency matrix for the network
**gn: a vector of group numbers, i.e. every node that has the number 1
to:
*Inputs: **A: the adjacency matrix for the network **gn: a vector of group numbers, i.e. every node that has the number 1
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**epsilon: the accuracy at which you would like the algorithm to act,
to:
**epsilon: the accuracy at which you would like the algorithm to act,
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**seed: the number used in the random number generator for placing the
to:
**seed: the number used in the random number generator for placing the
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**factor1: the number to multiply the community coordinates by, 2 tends to
to:
**factor1: the number to multiply the community coordinates by, 2 tends to
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**factor2: the number to multiply the node coordinates by within the
to:
**factor2: the number to multiply the node coordinates by within the
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*Outputs:
**xynew: the vectors of the xy coordinates for each node in your network, this
to:
*Outputs: **xynew: the vectors of the xy coordinates for each node in your network, this
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**XY: the vectors of xy coordinates for the communties, this is one of
to:
**XY: the vectors of xy coordinates for the communties, this is one of
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**xyc: the cellarray of xy coordinates for the nodes within each community
to:
**xyc: the cellarray of xy coordinates for the nodes within each community
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*Calling this Function (either of the commands below):
**[xynew, XY, xyc]=FRKK(A,gn,epsilon) **[xynew, XY, xyc]=FRKK(A,gn,epsilon, seed, factor1, factor2)
to:
*Calling this Function (either of the commands below): **[xynew, XY, xyc]=FRKK(A,gn,epsilon) **[xynew, XY, xyc]=FRKK(A,gn,epsilon, seed, factor1, factor2)
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**A: the adjacency matrix for the network
**gn: a list of group numbers for identifying each node by the community
to:
**A: the adjacency matrix for the network **gn: a list of group numbers for identifying each node by the community
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**epsilon: determines how precise you want to be in your node placement.
to:
**epsilon: determines how precise you want to be in your node placement.
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**seed: the seed for the random number generator for the initial
to:
**seed: the seed for the random number generator for the initial
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*Calling this function: **[xynew]=KKFR(A,gn,epsilon, seed)
#fruc_reinC (Fruchterman and Reingold placement of communities and then Fruchterman and Reingold placement of nodes within communities) [[(Attach:)fruc_reinC.m]]
This function uses the FruchtermanReingold Algorithm to find the optimal node placement for a given network with respect to Communities.
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**[xynew]=KKFR(A,gn,epsilon, seed)
#fruc_reinC (Fruchterman and Reingold placement of communities and then Fruchterman and Reingold placement of nodes within communities) [[(Attach:)fruc_reinC.m]]
This function uses the FruchtermanReingold Algorithm to find the optimal node placement for a given network with respect to Communities.
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**A: the adjacency matrix for the network
**gn: a vector of group numbers, i.e. every node that has the number 1
to:
**A: the adjacency matrix for the network **gn: a vector of group numbers, i.e. every node that has the number 1
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**epsilon: the accuracy at which you would like the algorithm to act,
to:
**epsilon: the accuracy at which you would like the algorithm to act,
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**seed: the number used in the random number generator for placing the
to:
**seed: the number used in the random number generator for placing the
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**factor1: the number to multiply the community coordinates by, 2 tends to
to:
**factor1: the number to multiply the community coordinates by, 2 tends to
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**factor2: the number to multiply the node coordinates by within the
to:
**factor2: the number to multiply the node coordinates by within the
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*Outputs:
**xynew: the vectors of the xy coordinates for each node in your network, this
to:
*Outputs: **xynew: the vectors of the xy coordinates for each node in your network, this
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**XY: the vectors of xy coordinates for the communties, this is one of
to:
**XY: the vectors of xy coordinates for the communties, this is one of
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**xyc: the cellarray of xy coordinates for the nodes within each community
to:
**xyc: the cellarray of xy coordinates for the nodes within each community
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to:
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**A: the adjacency matrix for the network
**gn: a list of group numbers for identifying each node by the community
to:
**A: the adjacency matrix for the network **gn: a list of group numbers for identifying each node by the community
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**epsilon: determines how precise you want to be in your node placement.
to:
**epsilon: determines how precise you want to be in your node placement.
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to:
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*Calling this Function:
**xy = KamadaC(A,maxgroups,.01)
to:
*Calling this Function: **xy = KamadaC(A,maxgroups,.01)
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*Inputs:
**A:the adjacency matrix of your network
**epsilon: the accuracy at which the algorithm acts, .01 works well.
*Outputs:
**xynew: the matrix of xy coordinates for each node.
*Calling this Function:
**xynew = Kamada(A,epsilon)
to:
*Inputs: **A:the adjacency matrix of your network **epsilon: the accuracy at which the algorithm acts, .01 works well. *Outputs: **xynew: the matrix of xy coordinates for each node. *Calling this Function: **xynew = Kamada(A,epsilon)
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*Inputs:
**A: the adjacency matrix for the network
**eps: is the accuracy at which the algorithm will act; .01 works well in
to:
*Inputs: **A: the adjacency matrix for the network **eps: is the accuracy at which the algorithm will act; .01 works well in
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**seed: the seed for the random number generator for initial placement of
to:
**seed: the seed for the random number generator for initial placement of
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*Outputs:
**xy: the matrix of xy coordinates for each node in your network,
to:
*Outputs: **xy: the matrix of xy coordinates for each node in your network,
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*Calling this Function:
**[xy] = fruc_rein(A,eps, seed)
to:
*Calling this Function: **[xy] = fruc_rein(A,eps, seed)
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[[(Attach:)nonlinear.pdf  One Page Paper for Chaos]] (preprint version)
Its associated winning [[(Attach:)Poster.pdf  poster]] from the Gallery of Nonlinear Image at the 2009 APS March Meeting
to:
*[[(Attach:)nonlinear.pdf  One Page Paper for Chaos]] (preprint version)
*Its associated winning [[(Attach:)Poster.pdf  poster]] from the Gallery of Nonlinear Image at the 2009 APS March Meeting
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All of this code requires that your adjacency matrix(or matrix that shows the connections between people) be sparse. To do this you can take your already created matrix A in Matlab and type sparse(A). This code also requires that you download the package from this website:
http://www.mathworks.com/matlabcentral/fileexchange/10922
Typing help and then the name of each program below gives specific instructions on the inputs and outputs of each program, which are also shown below.
*FRKK (FruchtermanReingold placement of communities, KamadaKawai placement of nodes within each community) [[(Attach:)FRKK.m]]
to:
This MATLAB code requires that your adjacency matrix (or matrix that shows the connections between people) is sparse. To create a sparse matrix in MATLAB, take your matrix (suppose it's called "A") "type sparse(A)". This code also requires that you download the package from [[http://www.mathworks.com/matlabcentral/fileexchange/10922  this website]].
Typing "help" and then the name of each program below gives specific instructions on the inputs and outputs of each program. We also show these here:
#FRKK (FruchtermanReingold placement of communities, KamadaKawai placement of nodes within each community) [[(Attach:)FRKK.m]]
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Inputs:
A is the adjacency matrix for the network
gn are a vector of group numbers, i.e. every node that has the number 1
to:
*Inputs:
**A: the adjacency matrix for the network
**gn: a vector of group numbers, i.e. every node that has the number 1
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episilon is the accuracy at which you would like the algorithm to act,
to:
**epsilon: the accuracy at which you would like the algorithm to act,
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seed is the number used in the random number generator for placing the
to:
**seed: the number used in the random number generator for placing the
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factor1 is the number to multiply the community coordinates by, 2 tends to
to:
**factor1: the number to multiply the community coordinates by, 2 tends to
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factor2 is the number to multiply the node coordinates by within the
to:
**factor2: the number to multiply the node coordinates by within the
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Outputs:
xynew are the vectors of the xy coordinates for each node in your network, this
to:
*Outputs:
**xynew: the vectors of the xy coordinates for each node in your network, this
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XY are the vectors of xy coordinates for the communties, this is one of
to:
**XY: the vectors of xy coordinates for the communties, this is one of
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xyc is the cellarray of xy coordinates for the nodes within each community
to:
**xyc: the cellarray of xy coordinates for the nodes within each community
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Calling this Function:
[xynew, XY, xyc]=FRKK(A,gn,epsilon) or [xynew, XY, xyc]=FRKK(A,gn,epsilon, seed, factor1, factor2)
*KKFR (KamadaKawai placement of communities, FruchtermanReingold placement of nodes within communities) [[(Attach:)KKFR.m]]
to:
*Calling this Function (either of the commands below):
**[xynew, XY, xyc]=FRKK(A,gn,epsilon) **[xynew, XY, xyc]=FRKK(A,gn,epsilon, seed, factor1, factor2)
#KKFR (KamadaKawai placement of communities, FruchtermanReingold placement of nodes within communities) [[(Attach:)KKFR.m]]
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Inputs:
A is the adjacency matrix for the network
gn is a list of group numbers for identifying each node by the community they belong in. For example, if you have 20 nodes, gn should be a 20x1
to:
*Inputs:
**A: the adjacency matrix for the network
**gn: a list of group numbers for identifying each node by the community they belong in. For example, if you have 20 nodes, gn should be a 20 x 1
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epsilon determines how precise you want to be in your node placement.
to:
**epsilon: determines how precise you want to be in your node placement.
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seed is the seed for the random number generator for the initial
to:
**seed: the seed for the random number generator for the initial
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Outputs: xynew is the set of coordinates for each node, the first column is the x coordinates and the second column is the ycoordinates.
Calling this function:
to:
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**xynew: the set of coordinates for each node, the first column is the x coordinates and the second column is the ycoordinates.
*Calling this function:
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[xynew]=KKFR(A,gn,epsilon, seed)
*fruc_reinC (Fruchterman and Reingold placement of communities and then Fruchterman and Reingold placement of nodes within communities) [[(Attach:)fruc_reinC.m]]
to:
**[xynew]=KKFR(A,gn,epsilon, seed)
#fruc_reinC (Fruchterman and Reingold placement of communities and then Fruchterman and Reingold placement of nodes within communities) [[(Attach:)fruc_reinC.m]]
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Inputs:
A is the adjacency matrix for the network
gn are a vector of group numbers, i.e. every node that has the number 1
to:
*Inputs:
**A: the adjacency matrix for the network
**gn: a vector of group numbers, i.e. every node that has the number 1
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episilon is the accuracy at which you would like the algorithm to act,
to:
**epsilon: the accuracy at which you would like the algorithm to act,
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seed is the number used in the random number generator for placing the
to:
**seed: the number used in the random number generator for placing the
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factor1 is the number to multiply the community coordinates by, 2 tends to
to:
**factor1: the number to multiply the community coordinates by, 2 tends to
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factor2 is the number to multiply the node coordinates by within the
to:
**factor2: the number to multiply the node coordinates by within the
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Outputs:
xynew are the vectors of the xy coordinates for each node in your network, this
to:
*Outputs:
**xynew: the vectors of the xy coordinates for each node in your network, this
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XY are the vectors of xy coordinates for the communties, this is one of
to:
**XY: the vectors of xy coordinates for the communties, this is one of
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xyc is the cellarray of xy coordinates for the nodes within each community
to:
**xyc: the cellarray of xy coordinates for the nodes within each community
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Calling this Function: [xynew, XY, xyc] = fruc_reinC(A, gn, epsilon, seed, factor1, factor2)
*KamadaC (KamadaKawai placement of communities and then KamadaKawai placement of nodes within communities) [[(Attach:)KamadaC.m]]
to:
*Calling this Function: **[xynew, XY, xyc] = fruc_reinC(A, gn, epsilon, seed, factor1, factor2)
#KamadaC (KamadaKawai placement of communities and then KamadaKawai placement of nodes within communities) [[(Attach:)KamadaC.m]]
Deleted lines 149156:
Inputs:
A is the adjacency matrix for the network
gn is a list of group numbers for identifying each node by the community they belong in. For example, if you have 20 nodes, gn should be a 20x1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2. You can use any community detection program you see fit.
Changed lines 151159 from:
epsilon determines how precise you want to be in your node placement. We suggest epsilon being .01
Outputs: xynew this is the list of xy coordinates for each node in your network.
Calling this Function:
to:
*Inputs:
**A: the adjacency matrix for the network
**gn: a list of group numbers for identifying each node by the community they belong in. For example, if you have 20 nodes, gn should be a 20 x 1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2. You can use any community detection program you see fit.
Changed lines 160165 from:
xy = KamadaC(A,maxgroups,.01)
*Kamada (KamadaKawai placement of nodes, ignoring community structure) [[(Attach:)Kamada.m]]
to:
**epsilon: determines how precise you want to be in your node placement. We suggest epsilon = .01
*Outputs: **xynew: is the list of xy coordinates for each node in your network.
*Calling this Function:
**xy = KamadaC(A,maxgroups,.01)
#Kamada (KamadaKawai placement of nodes, ignoring community structure) [[(Attach:)Kamada.m]]
Changed lines 181197 from:
Inputs:
A is the adjacency matrix of your network
epsilon is the accuracy at which the algorithm acts, .01 works well.
Outputs:
xynew is the matrix of xy coordinates for each node.
Calling this Function:
xynew = Kamada(A,epsilon)
*fruc_rein (Fruchterman and Reingold placement of nodes, ignoring community structure) [[(Attach:)fruc_rein.m]]
to:
*Inputs:
**A:the adjacency matrix of your network
**epsilon: the accuracy at which the algorithm acts, .01 works well.
*Outputs:
**xynew: the matrix of xy coordinates for each node.
*Calling this Function:
**xynew = Kamada(A,epsilon)
#fruc_rein (Fruchterman and Reingold placement of nodes, ignoring community structure) [[(Attach:)fruc_rein.m]]
Changed lines 201205 from:
Inputs:
A is the adjacency matrix for the network
eps is the accuracy at which the algorithm will act, .01 works well in
to:
*Inputs:
**A: the adjacency matrix for the network
**eps: is the accuracy at which the algorithm will act; .01 works well in
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seed is the seed for the random number generator for initial placement of
to:
**seed: the seed for the random number generator for initial placement of
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Outputs:
xy is the matrix of xy coordinates for each node in your network,
to:
*Outputs:
**xy: the matrix of xy coordinates for each node in your network,
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Calling this Function:
[xy] = fruc_rein(A,eps, seed)
Each of these programs has xy coordinates as one of the outputs which can be put into the below program to produce the graphs that are featured in the above article: [[(Attach:)graphplot2D.m]]
to:
*Calling this Function:
**[xy] = fruc_rein(A,eps, seed)
Each of these programs has xy coordinates as one of the outputs which can be put into the following program to produce the graphs that are featured in our Nonlinear Science Gallery poster and paper: [[(Attach:)graphplot2D.m]]
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[[(Attach:)nonlinear.pdf  One Page Paper for Chaos]] (preprint version) and its associated winning [[(Attach:)Poster.pdf  poster]] from the Gallery of Nonlinear Image at the 2009 APS March Meeting
to:
[[(Attach:)nonlinear.pdf  One Page Paper for Chaos]] (preprint version)
Its associated winning [[(Attach:)Poster.pdf  poster]] from the Gallery of Nonlinear Image at the 2009 APS March Meeting
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This is the page for the Visualization Code Featured in the below publications:
Gallery of Nonlinear Images Winner
[[(Attach:)Poster.pdf]]
One Page Paper for Chaos
[[(Attach:)nonlinear.pdf]]
to:
The visualization code on this page is featured in the following publication:
[[(Attach:)nonlinear.pdf  One Page Paper for Chaos]] (preprint version) and its associated winning [[(Attach:)Poster.pdf  poster]] from the Gallery of Nonlinear Image at the 2009 APS March Meeting

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to:
'''References'''
Tomihisa Kamada, Satoru Kawai.: An Algorithm for Drawing General Undirected Graphs. Information Processing Letters, 31:715, 1988.
Fruchtermann, T.M.J., Reingold, E.M.: Graph drawing by forcedirected placement. Software, Practice and Experience 21:11291164, 1991.
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The community detection program LeadEigs.m spits out the gn as maxgroups. This can be found on the KamadaKawai page at http://netwiki.amath.unc.edu/Code/KamadaKawai
to:
You may use any community detection program you see fit.
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After running LeadEigs.m on your symmetric sparse matrix, the proper prompt to run this program is the following:
to:
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seed is the seed for the random number generator for intial placement of
to:
seed is the seed for the random number generator for initial placement of
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to:
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*FRKK (Fruchterman and Reingold placement of communities, Kamada Kawaii placement of nodes within each community) [[(Attach:)FRKK.m]]
to:
*FRKK (FruchtermanReingold placement of communities, KamadaKawai placement of nodes within each community) [[(Attach:)FRKK.m]]
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uses the Kamada Kawai algorithm to place the nodes within each community.
to:
uses the KamadaKawai algorithm to place the nodes within each community.
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*KKFR (Kamada Kawaii placement of communities, Fruchterman and Reingold placement of nodes within communities) [[(Attach:)KKFR.m]]
to:
*KKFR (KamadaKawai placement of communities, FruchtermanReingold placement of nodes within communities) [[(Attach:)KKFR.m]]
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Graphs" while respecting individual communities. It uses the Fructerman and Reingold algorithm to place the nodes within each community.
to:
Graphs" while respecting individual communities. It uses the FruchtermanReingold algorithm to place the nodes within each community.
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*KamadaC (Kamada Kawaii placement of communities and then Kamada Kawaii placement of nodes within communities) [[(Attach:)KamadaC.m]]
to:
*KamadaC (KamadaKawai placement of communities and then KamadaKawai placement of nodes within communities) [[(Attach:)KamadaC.m]]
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This code outputs the xy coordinants for the nodes of a given network
to:
This code outputs the xy coordinates for the nodes of a given network
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The community detection program LeadEigs.m spits out the gn as maxgroups.
This can be found on the KamadaKaawai page at http://netwiki.amath.unc.edu/Code/KamadaKawai
to:
You can use any community detection program you see fit.
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After running LeadEigs.m on your symmetric sparse matrix, the proper prompt to run this program is the following:
to:
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*Kamada (Kamada Kawaii placement of nodes, ignoring community structure) [[(Attach:)Kamada.m]]
to:
*Kamada (KamadaKawai placement of nodes, ignoring community structure) [[(Attach:)Kamada.m]]
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[[(Attach:)graphplot2D.m]]
to:
[[(Attach:)graphplot2D.m]]
'''References''
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to:
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eps is the accuracy at which the algorith will act, .01 works well in
to:
eps is the accuracy at which the algorithm will act, .01 works well in
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to:
This code follows the algorithm in the article written by Tomihisa Kamada and Satoru Kawai entitled "An Algorithm For Drawing General Undirected Graphs."
Inputs:
A is the adjacency matrix of your network
epsilon is the accuracy at which the algorithm acts, .01 works well.
Outputs:
xynew is the matrix of xy coordinates for each node.
Calling this Function:
xynew = Kamada(A,epsilon)
Added lines 201222:
This function uses the FruchtermanReingold Algorithm to find the optimal node placement for a given network.
Inputs:
A is the adjacency matrix for the network
eps is the accuracy at which the algorith will act, .01 works well in most cases.
seed is the seed for the random number generator for intial placement of the nodes.
Outputs:
xy is the matrix of xy coordinates for each node in your network, the first column is the x coordinates and the second column is the y coordinates.
Calling this Function:
[xy] = fruc_rein(A,eps, seed)
Added lines 147177:
This code follows the algorithm in the article written by Tomihisa Kamada and Satoru Kawai entitled "An Algorithm For Drawing General Undirected Graphs" while respecting individual communities.
This code outputs the xy coordinants for the nodes of a given network Inputs:
A is the adjacency matrix for the network
gn is a list of group numbers for identifying each node by the community they belong in. For example, if you have 20 nodes, gn should be a 20x1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2. The community detection program LeadEigs.m spits out the gn as maxgroups. This can be found on the KamadaKaawai page at http://netwiki.amath.unc.edu/Code/KamadaKawai
epsilon determines how precise you want to be in your node placement. We suggest epsilon being .01
Outputs: xynew this is the list of xy coordinates for each node in your network.
Calling this Function:
After running LeadEigs.m on your symmetric sparse matrix, the proper prompt to run this program is the following: xy = KamadaC(A,maxgroups,.01)
Added lines 179180:
Added lines 182183:
Changed lines 3952 from:
factor1 is the number to multiply the community coordinates by, 2 tends to work well, but you may want the communities farther apart.
factor2 is the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases but you may want to change it based on your data.
Outputs:
xynew are the vectors of the xy coordinates for each node in your network, this is one of the inputs for graphplot2d.m
to:
factor1 is the number to multiply the community coordinates by, 2 tends to work well, but you may want the communities farther apart.
factor2 is the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases but you may want to change it based on your data.
Outputs:
xynew are the vectors of the xy coordinates for each node in your network, this is one of the inputs for graphplot2d.m
Changed lines 5661 from:
xyc is the cellarray of xy coordinates for the nodes within each community that are centered at zero.
Calling this Function:
[xynew, XY, xyc]=FRKK(A,gn,epsilon)
to:
xyc is the cellarray of xy coordinates for the nodes within each community that are centered at zero.
Calling this Function:
[xynew, XY, xyc]=FRKK(A,gn,epsilon)
Changed lines 6367 from:
[xynew, XY, xyc]=FRKK(A,gn,epsilon, seed, factor1, factor2)
to:
[xynew, XY, xyc]=FRKK(A,gn,epsilon, seed, factor1, factor2)
Changed lines 7095 from:
This code follows the algorithm in the article written by Tomihisa Kamada and Satoru Kawai entitled "An Algorithm For Drawing General Undirected Graphs" while respecting individual communities. It uses the Fructerman and Reingold algorithm to place the nodes within each community.
This code outputs the xy coordinates for the nodes of a given network Inputs:
A is the adjacency matrix for the network
gn is a list of group numbers for identifying each node by the community they belong in. For example, if you have 20 nodes, gn should be a 20x1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2. The community detection program LeadEigs.m spits out the gn as maxgroups. This can be found on the KamadaKawai page at http://netwiki.amath.unc.edu/Code/KamadaKawai
epsilon determines how precise you want to be in your node placement. We suggest epsilon being .01
seed is the seed for the random number generator for the initial placement of the communities
Outputs: xynew is the set of coordinates for each node, the first column is the x coordinates and the second column is the y coordinates.
Calling this function:
to:
This code follows the algorithm in the article written by Tomihisa Kamada and Satoru Kawai entitled "An Algorithm For Drawing General Undirected Graphs" while respecting individual communities. It uses the Fructerman and Reingold algorithm to place the nodes within each community.
This code outputs the xy coordinates for the nodes of a given network Inputs:
A is the adjacency matrix for the network
gn is a list of group numbers for identifying each node by the community they belong in. For example, if you have 20 nodes, gn should be a 20x1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2. The community detection program LeadEigs.m spits out the gn as maxgroups. This can be found on the KamadaKawai page at http://netwiki.amath.unc.edu/Code/KamadaKawai
epsilon determines how precise you want to be in your node placement. We suggest epsilon being .01
seed is the seed for the random number generator for the initial placement of the communities
Outputs: xynew is the set of coordinates for each node, the first column is the x coordinates and the second column is the ycoordinates.
Calling this function:
Changed lines 98103 from:
After running LeadEigs.m on your symmetric sparse matrix, the proper prompt to run this program is the following: [xynew]=KKFR(A,gn,epsilon, seed)
to:
After running LeadEigs.m on your symmetric sparse matrix, the proper prompt to run this program is the following: [xynew]=KKFR(A,gn,epsilon, seed)
Added lines 105145:
This function uses the FruchtermanReingold Algorithm to find the optimal node placement for a given network with respect to Communities. Inputs:
A is the adjacency matrix for the network
gn are a vector of group numbers, i.e. every node that has the number 1 is in group 1.
episilon is the accuracy at which you would like the algorithm to act, .01 tends to work well.
seed is the number used in the random number generator for placing the communities, any four digit number will work.
factor1 is the number to multiply the community coordinates by, 2 tends to work well, but you may want the communities farther apart.
factor2 is the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases but you may want to change it based on your data.
Outputs:
xynew are the vectors of the xy coordinates for each node in your network, this is one of the inputs for graphplot2d.m
XY are the vectors of xy coordinates for the communties, this is one of the inputs for drawForceCPie.m
xyc is the cellarray of xy coordinates for the nodes within each community that are centered at zero.
Calling this Function: [xynew, XY, xyc] = fruc_reinC(A, gn, epsilon, seed, factor1, factor2)
Changed lines 2225 from:
to:
This function uses the FruchtermanReingold Algorithm to find the optimal node placement for a given network with respect to Communities. But then uses the Kamada Kawai algorithm to place the nodes within each community.
Inputs:
A is the adjacency matrix for the network
gn are a vector of group numbers, i.e. every node that has the number 1 is in group 1.
episilon is the accuracy at which you would like the algorithm to act, .01 tends to work well.
seed is the number used in the random number generator for placing the communities, any four digit number will work.
factor1 is the number to multiply the community coordinates by, 2 tends to work well, but you may want the communities farther apart.
factor2 is the number to multiply the node coordinates by within the community, if there are more than five nodes within a particular community it is helpful to normalize the coordinates and then multiply them by a factor to redistribute them. 4 tends to work in most cases but you may want to change it based on your data.
Outputs:
xynew are the vectors of the xy coordinates for each node in your network, this is one of the inputs for graphplot2d.m
XY are the vectors of xy coordinates for the communties, this is one of the inputs for drawForceCPie.m
xyc is the cellarray of xy coordinates for the nodes within each community that are centered at zero.
Calling this Function:
[xynew, XY, xyc]=FRKK(A,gn,epsilon) or [xynew, XY, xyc]=FRKK(A,gn,epsilon, seed, factor1, factor2)
Added lines 69102:
This code follows the algorithm in the article written by Tomihisa Kamada and Satoru Kawai entitled "An Algorithm For Drawing General Undirected Graphs" while respecting individual communities. It uses the Fructerman and Reingold algorithm to place the nodes within each community.
This code outputs the xy coordinates for the nodes of a given network Inputs:
A is the adjacency matrix for the network
gn is a list of group numbers for identifying each node by the community they belong in. For example, if you have 20 nodes, gn should be a 20x1 matrix. If node 5 is in the 2nd community, then at (5,1) the value is 2. The community detection program LeadEigs.m spits out the gn as maxgroups. This can be found on the KamadaKawai page at http://netwiki.amath.unc.edu/Code/KamadaKawai
epsilon determines how precise you want to be in your node placement. We suggest epsilon being .01
seed is the seed for the random number generator for the initial placement of the communities
Outputs: xynew is the set of coordinates for each node, the first column is the x coordinates and the second column is the y coordinates.
Calling this function: After running LeadEigs.m on your symmetric sparse matrix, the proper prompt to run this program is the following: [xynew]=KKFR(A,gn,epsilon, seed)
Changed lines 1314 from:
All of this code requires that your adjacency matrix be sparse, and that you download the package from this website:
to:
All of this code requires that your adjacency matrix(or matrix that shows the connections between people) be sparse. To do this you can take your already created matrix A in Matlab and type sparse(A). This code also requires that you download the package from this website:
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Typing help and then the name of the program gives specific instructions on the inputs and outputs of each program.
to:
Typing help and then the name of each program below gives specific instructions on the inputs and outputs of each program, which are also shown below.
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Typing help and then the name of the program gives specific instructions on the inputs and outputs of each program.
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*fruc_rein (Fruchterman and Reingold placement of nodes, ignoring community structure) [[(Attach:)fruc_rein.m]]
to:
*fruc_rein (Fruchterman and Reingold placement of nodes, ignoring community structure) [[(Attach:)fruc_rein.m]]
Each of these programs has xy coordinates as one of the outputs which can be put into the below program to produce the graphs that are featured in the above article: [[(Attach:)graphplot2D.m]]
Changed line 11 from:
to:
Changed lines 1819 from:
*FRKK (Fruchterman and Reingold placement of communities, Kamada Kawaii placement of nodes within each community) [[(Attach:)FRKK.m]]
to:
*FRKK (Fruchterman and Reingold placement of communities, Kamada Kawaii placement of nodes within each community) [[(Attach:)FRKK.m]]
Changed line 21 from:
*KamadaC (Kamada Kawaii placement of communities and then Kamada Kawaii placement of nodes within communities) [(Attach:)KamadaC.m]]
to:
*KamadaC (Kamada Kawaii placement of communities and then Kamada Kawaii placement of nodes within communities) [[(Attach:)KamadaC.m]]
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to:
Deleted line 17:
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to:
[[(Attach:)FRKK.m]] *KKFR (Kamada Kawaii placement of communities, Fruchterman and Reingold placement of nodes within communities) [[(Attach:)KKFR.m]] *fruc_reinC (Fruchterman and Reingold placement of communities and then Fruchterman and Reingold placement of nodes within communities) [[(Attach:)fruc_reinC.m]] *KamadaC (Kamada Kawaii placement of communities and then Kamada Kawaii placement of nodes within communities) [(Attach:)KamadaC.m]] *Kamada (Kamada Kawaii placement of nodes, ignoring community structure) [[(Attach:)Kamada.m]] *fruc_rein (Fruchterman and Reingold placement of nodes, ignoring community structure) [[(Attach:)fruc_rein.m]]
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to:
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'''FRKK (Fruchterman and Reingold placement of communities, Kamada Kawaii placement of nodes within each community)
to:
*FRKK (Fruchterman and Reingold placement of communities, Kamada Kawaii placement of nodes within each community)
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This is the page at which the visualization code will eventually be!
to:
This is the page for the Visualization Code Featured in the below publications:
Changed lines 820 from:
[[(Attach:)nonlinear.pdf]]
to:
[[(Attach:)nonlinear.pdf]]
''Code
All of this code requires that your adjacency matrix be sparse, and that you download the package from this website:
http://www.mathworks.com/matlabcentral/fileexchange/10922
'''FRKK (Fruchterman and Reingold placement of communities, Kamada Kawaii placement of nodes within each community) [[(Attach:)FRKK.m]]
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This is the page at which the visualization code will eventually be!
to:
This is the page at which the visualization code will eventually be!
Gallery of Nonlinear Images Winner [[(Attach:)Poster.pdf]]
One Page Paper for Chaos [[(Attach:)nonlinear.pdf]]
Added line 1:
This is the page at which the visualization code will eventually be!


