...
We need to start somewhere. Let's create a new network called "net" with a single node or vertex.
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# # Learning to use network and network plotting functions for VIVO data # # # M. Conlon April 11,2011 #start with a root node net 2011 # # # start with a root node net <-network.initialize(1,directed=F) net %v% "type" <- "Root" net %v% "vertex.names" <- "COM" net %v% "amt" <-0 |
That's
...
all
...
you
...
need
...
to
...
create
...
a
...
single
...
node
...
network
...
with
...
some
...
attributes.
...
The
...
network
...
has
...
one
...
node
...
with
...
name
...
"COM"
...
(that's
...
short
...
for
...
"College
...
of
...
Medicine"
...
at
...
UF).
...
The
...
%v%
...
lines
...
set
...
various
...
attribute
...
lists
...
to
...
singleton
...
values.
...
Here
...
I've
...
started
...
a
...
"type"
...
attribute
...
to
...
differentiate
...
the
...
types
...
of
...
things
...
that
...
will
...
be
...
in
...
this
...
network
...
– Departments,
...
people,
...
grants,
...
and
...
a
...
single
...
root
...
vertex.
...
The
...
amt
...
attribute
...
will
...
hold
...
dollar
...
amounts
...
for
...
grants
...
and
...
totals
...
for
...
grants
...
at
...
other
...
levels
...
in
...
the
...
network.
...
Adding
...
Departments
...
to
...
the
...
Network
...
Next,
...
let's
...
create
...
three
...
departments.
...
Two
...
lines
...
of
...
code
...
are
...
needed
...
for
...
each
...
department.
...
First,
...
add
...
a
...
vertex
...
to
...
the
...
network.
...
Note
...
that
...
that
...
the
...
same
...
attributes
...
are
...
being
...
set
...
for
...
each
...
vertex
...
– type,vertex.names
...
and
...
amt.
...
Second,
...
the
...
vertex
...
is
...
linked
...
to
...
the
...
root
...
by
...
asserting
...
an
...
edge
...
connecting
...
the
...
department
...
to
...
the
...
root.
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# {panel} # |
...
create some departments |
...
# |
...
add.vertices(net,1,list(list(type="Department",vertex.names="Radiology",amt=0))) |
...
net["COM","Radiology"]<-1 |
...
add.vertices(net,1,list(list(type="Department",vertex.names="Pathology",amt=0))) |
...
net["COM","Pathology"]<-1 |
...
add.vertices(net,1,list(list(type="Department",vertex.names="Pediatrics",amt=0))) |
...
net["COM","Pediatrics"]<-1 |
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{panel}
Edge assertions are simple matrix element assignments in an edge matrix with the same name as the network object. This is all provided by the statnet package. From and to are specified by vertex names. The value "1" indicates an edge between the vertices.
There is much more that can be done with edges and edge attributes. We are keeping things simple here.
h1. Adding People to the Departments
Let's add people to each of the departments. We use the same method as previously -- create a vertex for each person and connect it to the department vertex.
{panel}
|
...
|
Edge assertions are simple matrix element assignments in an edge matrix with the same name as the network object. This is all provided by the statnet package. From and to are specified by vertex names. The value "1" indicates an edge between the vertices.
There is much more that can be done with edges and edge attributes. We are keeping things simple here.
Adding People to the Departments
Let's add people to each of the departments. We use the same method as previously – create a vertex for each person and connect it to the department vertex.
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#
# create some people in the departments
#
|
...
add.vertices(net,1,list(list(type="Person",vertex.names="Mancuso",amt=0))) |
...
net["Radiology","Mancuso"]<-1 |
...
add.vertices(net,1,list(list(type="Person",vertex.names="Sistrom",amt=0))) |
...
net["Radiology","Sistrom"]<-1 |
...
add.vertices(net,1,list(list(type="Person",vertex.names="Rathe",amt=0))) |
...
net["Radiology","Rathe"]<-1 |
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{panel}
There. We have added three people to the Radiology Department.
In the code below, we first add 15 people to Pathology and 30 people to Pediatrics. We use an R loop to repetitively add the vertices and edges. We generate a "name" for each person using the R paste function. The people in Pathology will be named P1, P2, ... P15. The people in Pediatrics will be named M1, M2, ... M30.
Experienced R programmers will ask "Do we need loops? Why not add all the Pediatric vertices at once and then all the Pediatric edges at once?" And the answer is "Of course." We have simplified the code for the presentation here. R programmers would figure out how to add 15 Pediatric people in two lines -- one for vertices and one for edges.
{panel}
|
...
|
There. We have added three people to the Radiology Department.
In the code below, we first add 15 people to Pathology and 30 people to Pediatrics. We use an R loop to repetitively add the vertices and edges. We generate a "name" for each person using the R paste function. The people in Pathology will be named P1, P2, ... P15. The people in Pediatrics will be named M1, M2, ... M30.
Experienced R programmers will ask "Do we need loops? Why not add all the Pediatric vertices at once and then all the Pediatric edges at once?" And the answer is "Of course." We have simplified the code for the presentation here. R programmers would figure out how to add 15 Pediatric people in two lines – one for vertices and one for edges.
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for (i in 1:15) { name <- paste("P",i,sep="") |
...
add.vertices(net,1,list(list(type="Person",vertex.names=name,amt=0))) |
...
net["Pathology",name]<-1 |
...
} |
...
for (i in 1:30) { |
...
name <- paste("M",i,sep="") |
...
add.vertices(net,1,list(list(type="Person",vertex.names=name,amt=0))) |
...
net["Pediatrics",name]<-1 |
...
} |
...
Adding
...
Grants
...
for
...
each
...
person
...
Finally,
...
we
...
wish
...
to
...
add
...
grant
...
vertices
...
and
...
an
...
edge
...
connecting
...
the
...
grant
...
vertex
...
to
...
a
...
corresponding
...
person.
...
Each
...
grant
...
will
...
have
...
a
...
name
...
(again
...
automatically
...
generated)
...
and
...
an
...
award
...
amount.
...
In
...
the
...
example,
...
the
...
award
...
amount
...
is
...
stored
...
in
...
the
...
attribute
...
"amt".
...
We generate
...
award
...
amounts
...
as
...
a
...
random
...
number.
...
We
...
have
...
a
...
bit
...
of
...
a
...
conundrum.
...
In
...
our
...
previous
...
work,
...
we
...
explicitly
...
added
...
code
...
for
...
for
...
each
...
department
...
and
...
then
...
for
...
each
...
department,
...
we
...
wrote
...
code
...
to
...
add
...
people
...
to
...
the
...
department.
...
It's
...
not
...
feasible
...
to
...
write
...
code
...
for
...
each
...
person
...
on
...
the
...
network
...
to
...
add
...
their
...
grants.
...
Even
...
in
...
the
...
simple
...
work
...
we
...
are
...
presenting
...
here,
...
we
...
now
...
have
...
48
...
people
...
in
...
the
...
network.
...
How
...
do
...
we
...
proceed?
...
The
...
code
...
sample
...
below
...
begins
...
by
...
creating
...
three
...
vectors
...
of
...
the
...
attributes
...
of
...
the
...
nodes
...
creates
...
so
...
far
...
– vt
...
stores
...
types,
...
vn
...
stores
...
names
...
and
...
va
...
stores
...
amounts
...
(all
...
zero
...
so
...
far,
...
we
...
have't
...
added
...
any
...
grants
...
yet).
...
The
...
rest
...
of
...
the
...
code
...
is
...
two
...
nested
...
loops.
...
The
...
outer
...
loop
...
scans
...
the
...
entire
...
network
...
looking
...
for
...
Person
...
nodes.
...
When
...
a
...
Person
...
nodes
...
is
...
found,
...
a
...
number
...
of
...
grants
...
(ng)
...
is
...
generated
...
as
...
a
...
random
...
from
...
0
...
to
...
9
...
using
...
the
...
R
...
runif
...
function.
...
Then
...
an
...
inner
...
loop
...
processes
...
each
...
grant:
...
- A
...
- grant
...
- name
...
- is
...
- generated
...
- A
...
- grant
...
- amount
...
- is
...
- generated
...
- using
...
- the
...
- R
...
- function
...
- rexp.
...
- And
...
- then
...
- the
...
- grant
...
- is
...
- added
...
- to
...
- the
...
- network
...
- just
...
- as
...
- the
...
- previous
...
- nodes
...
- were
...
- added
...
- – a
...
- call
...
- to
...
- call.vertices
...
- followed
...
- by
...
- adding
...
- an
...
- edge
...
- connecting
...
- the
...
- grant
...
- to
...
- the
...
- person.
...
Following
...
the
...
addition
...
of
...
the
...
grant
...
to
...
the
...
network,
...
three
...
short
...
sequences
...
of
...
code
...
update
...
the
...
amt
...
total
...
for
...
the
...
corresponding
...
person,
...
department
...
and
...
root
...
nodes.
...
The
...
amounts
...
are
...
retrieved,
...
the
...
corresponding
...
amt
...
is
...
added
...
and
...
the
...
amounts
...
are
...
put
...
back
...
in
...
the
...
network
...
object.
...
Again,
...
an
...
experienced
...
R
...
programmer
...
could
...
remove
...
steps.
...
The
...
code
...
shown
...
here
...
is
...
designed
...
for
...
clarity,
...
not
...
conciseness
...
nor
...
speed.
...
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# |
...
# |
...
Add grants as another level |
...
# |
...
vt<-get.vertex.attribute(net,"type") |
...
vn<-get.vertex.attribute(net,"vertex.names") |
...
va<-get.vertex.attribute(net,"amt") |
...
for (i in 1:network.size(net)) { |
...
if (vt |
...
[i] == "Person") |
...
{ di<-get.neighborhood(net,i)[1] # get department index for this |
...
person ng <- floor(10*runif(1)) |
...
if (ng > 0) |
...
{ for (j in 1:ng) |
...
{ gname <- paste(vn[i],".G",j,sep="") |
...
gamt=floor(50000*rexp(1)*rexp(1)) |
...
add.vertices(net,1,list(list(type="Grant",vertex.names=gname,amt=gamt))) |
...
net[vn[i],gname]<-1 |
...
# # Add the grant amounts to the corresponding person grant total # va<-get.vertex.attribute(net,"amt") |
...
va[i]<-va[i]+gamt |
...
set.vertex.attribute(net,"amt",va) |
...
# # Add the grant amounts to the corresponding department total # va<-get.vertex.attribute(net,"amt") |
...
va[di]<-va[di]+gamt |
...
set.vertex.attribute(net,"amt",va) |
...
# # Add the grant amount to the root total # va<-get.vertex.attribute(net,"amt") |
...
va[1]<-va[1]+gamt |
...
set.vertex.attribute(net,"amt",va) |
...
|
...
|
...
|
...
|
...
|
...
|
...
}
}
}
}
|
Whew. We not only added grants to each person, we created grant totals for persons, departments and the root (the collection of all departments). These grant totals represent the total number of dollars awarded to the person, to the persons in a department and to all the persons in all the departments. These totals will be very valuable in making displays and reports from the data.
Displaying the results
Now we can display the network using the built-in features of R and the statnet package. A plot can be generated with a single statement:
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plot(net)
|
The result is shown below:
Default network visualization
All the nodes are the same color. Perhaps we'd like to see the grants colored differently from the people and so on. This is easily done. The plot() function can use vertex attributes to control display. For example:
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|
...
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{panel}
The result is shown below:
!Simple.png! Default network visualization
All the nodes are the same color. Perhaps we'd like to see the grants colored differently from the people and so on. This is easily done. The plot() function can use vertex attributes to control display. For example:
{panel}
|
plot(net,vertex.col="type") |
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{panel}
The result is shown below:
!VerticesByType.png! Vertices colored by type. Departments are black. The root is blue
Finally, the code below assigns colors to vertices using the R palette functions (a rainbow palette). The plot displays the size of each node based on the value of the vertex attribute amt. So the root node will be biggest. A formula is used to scale the node sizes based on the log of grant dollars -- so 1 million dollars has size "6", a one thousand dollar grant has size "3", and so on.
{panel}
|
...
|
The result is shown below:
Vertices colored by type. Departments are black. The root is blue
Finally, the code below assigns colors to vertices using the R palette functions (a rainbow palette). The plot displays the size of each node based on the value of the vertex attribute amt. So the root node will be biggest. A formula is used to scale the node sizes based on the log of grant dollars – so 1 million dollars has size "6", a one thousand dollar grant has size "3", and so on.
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# # generate a plot of departments, people and grants # palette(rev(rainbow(6))) |
...
plot(net,vertex.col="type", |
...
vertex.cex=0.25*(1+floor(log10(1+get.vertex.attribute(net,"amt"))))) |
...
|
The result is shown below:
...
R and statnet provide many more options for displaying graphs and networks. If you are not familiar with R, you will want to review the help system regarding the plot function and the par (graphical parameters) function. It is straightforward to display labels, control the presentation of edges, use different shapes for vertices and much more.
Summary
...
The code provided here is for idea generation. What attributes would be good to show on such a plot? What other plots can be made? How would we display publications? Or multi-level organizational structure?
...