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Eigenvector values for different time periods of same network (igraph in R)


Eigenvector values for different time periods of same network (igraph in R)

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Eigenvector values for different time periods of same network (igraph in R)
Tag : r , By : scott.sizemore
Date : November 27 2020, 04:01 AM

I think the issue was by ths following , Thank you for providing an easily reproducible example. If I understand your problem correctly, all you need to do is:
iterate through every year filter out edges that don't have the edge attribute associated with the year you are iterating over calculate the eigenvalues for the filtered graph store the outputs in a single data frame
# install.packages('tidyverse')
library(tidyverse)


#let's get all unique values for year
#we can do this by pulling the edge attribute
#"year" frome the graph "network"
years <- E(network)$year %>%
  unique


#now we want to use purrr's map to iterate through all the years
#the goal is to only keep edges from a year we are interested in
#"map" returns a list, and if we use the function "setNames", then
#each item in the list will be named after the object we are iterating
eigen_by_year <- purrr::map(setNames(years, years), function(yr){
  #here we filter away all edges that aren't from the year we are interested
  network_filtered = network - E(network)[year != yr]

  #we now calculate the eigen values for the filtered network
  eigen_values <- eigen_centrality(network_filtered, directed = F)$vector

  #"eigen_values" is a named vector, let's convert this named vector
  #into a data frame with the name column being the vertex name
  #and the value column being the eigen value
  tibble::enframe(eigen_values)
})

#The result is a list where the item names are the years
#and they contain a data frame of the eigen values associated
#with their years
eigen_by_year

#let's use enframe one more time so that the name of the list items
#are now their own "name" column and the nested data rames are
#in the "value" column" we will need to use unnest to flatten the dataframe
eigen_data_frame <- eigen_by_year %>%
  tibble::enframe() %>%
  tidyr::unnest()

eigen_data_frame

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MDX Help: - Comparing Values in Two Max Time Periods within a larger Set of Time Periods to Populate an Indicator


Tag : sql-server-2008 , By : user179190
Date : March 29 2020, 07:55 AM
like below fixes the issue I don't know how much you can edit in the MDX expression - or in your report builder, but to get the difference between two values in a series, you can create a measure (in your report) that is the difference between the CurrentMember and PrevMember. Since the time series (timeid) is sorted by the key, it will always be in the right order (or your schema and architecture needs a rework)
So basically, you can do :
WITH 
MEMBER MEASURES.GrowthTime AS ( 
        ( [Measures].[Value], [TimeID].CurrentMember ) - 
        ( [Measures].[Value], [TimeID].PrevMember )
)
MEMBER MEASURES.GrowthRatio AS (
        ( [Measures].[Value], [TimeID].CurrentMember ) /
        ( [Measures].[Value], [TimeID].PrevMember )
)
SELECT { Measures.Value, Measures.GrowthTime, Measures.GrowthRatio } on 0,
[TimeID].CHILDREN on 1
FROM Cube

igraph (R) How to create correlation network with only strong r values


Tag : r , By : Liy
Date : March 29 2020, 07:55 AM
To fix this issue You can delete the edges from the graph if you want to, or delete them from the matrix in the first place. E.g.
cor_mat[ cor_mat < .8 ] <- 0
diag(cor_mat) <- 0
graph <- graph.adjacency(cor_mat, weighted=TRUE, mode="lower")
graph <- delete.edges(graph, E(graph)[ weight < 0.8 ])

Display only specific labels, using eigenvector centrality (igraph in R)


Tag : r , By : Tink
Date : March 29 2020, 07:55 AM
this one helps. I am using version 0.99.879 and version 1.0.1. , Take a look at the help page ?evcent. It says:
ifelse(evcent(g)$vector > 0.01,

How to plot a network degree to node size and eigenvector to x axis and an attribute to y axis in igraph in R


Tag : r , By : Tim
Date : March 29 2020, 07:55 AM
To fix this issue For those who want to work with this, an easier to execute version of the data is at the end of the post.
I am going to assume that you want to layout the size and position of the nodes.
VS = 6 + 5*degree(g)
plot(g, vertex.size=VS)
x = round(10*eigen_centrality(g)$vector, 2)
y = vertex_attr(g, "Pro")
LO = cbind(x,y)
plot(g, vertex.size=VS, layout=LO)
plot(g, vertex.size=7*VS, layout=LO, rescale=F, 
    xlim=range(x), ylim=range(y))
axis(side=1, pos=min(y)-1)
axis(side=2)
EL = read.table(text="Vertex.1 Vertex.2 Type
P702 P617 Trig
P617 P616 Aff
P619 P701 Inf
P212 P701 Inf
P701 P608 Aff
P701 P625 Aff
P619 P807 Trig
P623 P101 Inf
P613 P801 Inf
P619 P606 Inf
P606 P603 Aff
P602 P606 Aff
P615 P252 Inf
P603 P615 Inf
P251 P238 Aff
P604 P615 Inf
P604 P624 Inf",
header=T)

VERT = read.table(text="Vertex Property Pro
P702 7 5.0
P617 6 -4.0
P616 6 7.0
P619 7 -6.0
P701 7 6.0
P212 2 2.0
P608 6 3.0
P625 6 -5.0
P807 8 -4.0
P623 6 2.5
P101 1 1.6
P613 6 6.0
P801 8 3.0
P606 6 1.0
P603 6 -2.0
P602 6 -5.0
P615 6 4.5
P252 2 2.0
P251 2 -3.0
P238 2 2.0
P604 6 2.0
P624 6 1.0",
header=T)

g = graph_from_data_frame(EL, directed=FALSE, vertices=VERT)

Should out-linking nodes be in rows or columns for calculating eigenvector centrality of a directed graph using igraph i


Tag : r , By : lhoBas
Date : March 29 2020, 07:55 AM
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