Description Usage Arguments Details Value Author(s) References See Also Examples
kpset
helps identify the most central group of players in a social network given a sepcified centraliy measure and a target group size.
1 2 3 4 
adj.matrix 
Matrix indicating the adjacency matrix of the network or in the case of diffusion centrality a probability matrix. 
size 
Integer indicating the target size of players. 
type 
A string indicating the type of centrality measure to be used. Should be one of 
M 
Positive number indicating the maximum geodistance between two nodes,
above which the two nodes are considered disconnected. The default is 
T 
Integer indicating the maximum number of iterations in the communication process. By default, T is the network size. 
method 
Indication of which grouping criterion should be used. 
binary 
If 
cmode 
String indicating the type of centrality being evaluated.
The option is applicable to degree and Mreach centralities.

large 
Logical scalar. If 
geodist.precomp 
Geodistance precomputed for the network to be analyzed (optional). 
seed 
String indicating the seeding method or a vector of the seeds specified by user.
If 
parallel 
Logical scalar. IF 
cluster 
Integer indicating the number of CPU cores to be used for parallel computation. 
round 
Integer indicating the "length" of search, namely, the number of loops over the nodes in the candidate set. 
iteration 
Integer indicating the "width" of search in each round, namely, the number of loops over the nodes in the residual set. 
The most central group of players in a network is not necessarily the set of players who are the most central as individuals because there may be redundancy in their connections. Currenlty a greedy search algorithm is implemented in this package to identify the most central group of key players. The basic steps are shown as follows.
Select an initial candidate set C. The residual set is denoted as R.
Update the candidate set C.
Start with the first node in C. Try to swap it with nodes in R sequentially (loop 1). Make the swap if it improves the centrality score of the resulting C. The number of loop 1 is defined as the number of iterations (over the nodes in the residual set).
Repeat step 1 for each node in C sequentially (loop 2). The number of loop 2 is defined as the number of rounds (over the nodes in the candidate set).
Stop if (a) the change in C's centrality score is negligible (i.e. it is smaller than a prespecified threshold determined by both the network size and edge values.) or (b) the process reaches a specified number of rounds.
Return the final candidate set and the centrality score.
It is recommended to run kpset
several times with different seeds so that the algorithm will not be trapped in a local optimum.
To facilitate the search in large networks, users may specify a reasonable number of iterations
or rounds and/or utilize parallel computation. During parallel computation, for each cluster and each iteration
the algorithm randomly picks a node from the candidate set and the residual set, respectively,
and swaps the two if it improves the centrality score of the candidate set. It repeats this process until exhausting the specified iterations and rounds
and then compare and combine the results from the clusters.
kpset
returns the column indices of the players who form
the most central set and its centrality score.
Weihua An [email protected]; YuHsin Liu [email protected]
An, Weihua. (2015). "Multilevel Meta Network Analysis with Application to Studying Network Dynamics of Network Interventions." Social Networks 43: 4856.
An, Weihua and YuHsin Liu (2016). "keyplayer: An R Package for Locating Key Players in Social Networks."
Working Paper, Indiana Univeristy.
Borgatti, Stephen P. (2006). "Identifying Sets of Key Players in a Network." Computational, Mathematical and Organizational Theory, 12(1):2134.
Butts, Carter T. (2014). sna: Tools for Social Network Analysis. R package
version 2.32. http://CRAN.Rproject.org/package=sna
Csardi, G and Nepusz, T (2006). "The igraph software package for complex network research."
InterJournal, Complex Systems 1695. http://igraph.org
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  # Create a 5x5 weighted and directed adjacency matrix
W < matrix(
c(0,1,3,0,0,
0,0,0,4,0,
1,1,0,2,0,
0,0,0,0,3,
0,2,0,0,0),
nrow=5, ncol=5, byrow = TRUE)
# Find the most central player set sized 2 in terms of the degree centrality
kpset(W,size=2,type="degree")
# Find two most central players in terms of indegree
# via parallel computation using 5 cpu cores
kpset(W,size=2,type="degree", cmode="indegree", parallel = TRUE, cluster = 2)

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