| Type: | Package |
| Title: | Large-Scale Social Network Analysis |
| Version: | 1.0.0 |
| Description: | We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics. Details can be found in the accompanying scientific paper on the Journal of Statistical Software, <doi:10.18637/jss.v096.i07>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Imports: | doParallel (≥ 1.0.0), foreach (≥ 1.5.0), igraph (≥ 1.2.0), tidygraph(≥ 1.2.0) |
| Depends: | R (≥ 4.0.0) |
| Encoding: | UTF-8 |
| LazyData: | true |
| BugReports: | https://github.com/networkgroupR/fastnet/issues |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2020-12-01 02:20:58 UTC; xu |
| Author: | Nazrul Shaikh [aut, cre], Xu Dong [aut], Luis Castro [aut], Christian Llano [ctb] |
| Maintainer: | Nazrul Shaikh <networkgroupr@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2020-12-01 07:40:02 UTC |
Degrees of nodes
Description
Collect the degrees for all nodes in a network.
Usage
degree.collect(net)
Arguments
net |
The input network. |
Details
Obtain the degrees for all nodes.
Value
A vector.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
x.deg <- degree.collect(x)
summary(x.deg)
## End(Not run)
Plot of the degree distribution of a network
Description
Plot the degree distribution of a network.
Usage
degree.dist(net, cumulative = TRUE, log = TRUE)
Arguments
net |
The input network. |
cumulative |
A logical index asking whether a cumulative distribution should be returned. |
log |
A logical index asking whether a logarithm-scaled distribution should be returned. |
Details
Plot the degree distribution of a network.
Value
A .gif plot.
Author(s)
Xu Dong
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
## Plot the standard degree distribution of x.
degree.dist(x, cumulative = FALSE, log = FALSE)
## Plot the degree distribution of x, with a logarithm scale.
degree.dist(x, cumulative = FALSE, log = TRUE)
## Plot the cumulative degree distribution of x.
degree.dist(x, cumulative = TRUE, log = FALSE)
## Plot the cumulative degree distribution of x, with a logarithm scale.
degree.dist(x, cumulative = TRUE, log = TRUE)
## End(Not run)
Histogram of the degree distribution of a network
Description
Plot the histogram of all degrees of a network.
Usage
degree.hist(g, breaks = 100)
Arguments
g |
The input network. |
breaks |
A single number giving the number of cells for the histogram. |
Details
Plot the histogram of all degrees of a network.
Value
A .gif plot.
Author(s)
Xu Dong
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.05)
degree.hist(x)
## End(Not run)
Plot of a small network
Description
Plot a small network.
Usage
draw.net(net)
Arguments
net |
The input network. |
Details
Plot a small network.
Value
A .gif plot.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
x <- net.ring.lattice(12,4)
draw.net(x)
## End(Not run)
Adjacency Matrix to fastnet
Description
Transform an adjacency matrix to an ego-centric list form used in fastnet.
Usage
from.adjacency(adj.mat)
Arguments
adj.mat |
The input adjacency matrix |
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Christian Llano.
Examples
adj.mat <- matrix(c(0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0), nrow = 4, ncol = 4)
g <- from.adjacency(adj.mat)
Edgelist to fastnet
Description
Transform an edgelist to an ego-centric list form used in fastnet.
Usage
from.edgelist(edgelist)
Arguments
edgelist |
A 2-column data frame, in which the 1st column represents the start nodes, and the 2nd column represents the destination nodes. |
Details
Most network data repositories choose to store the data in an edgelist form. This function helps user to load it in fastnet.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong
Examples
edgelist <- data.frame(from=c(1, 3, 2, 3, 3), to=c(4, 5, 6, 5, 7))
g <- from.edgelist(edgelist)
Transform an igraph object to a fastnet object
Description
Transform an igraph object to an ego-centric list form used in fastnet.
Usage
from.igraph(net.igraph)
Arguments
net.igraph |
The input |
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong.
Examples
## Not run:
library("igraph")
net.igraph <- erdos.renyi.game(100,0.1)
g <- from.igraph(net.igraph)
## End(Not run)
statnet to fastnet
Description
Import a statnet object.
Usage
from.statnet(net.statnet, ncores = detectCores())
Arguments
net.statnet |
The input |
ncores |
The number of cores to be used. |
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong
Examples
## Not run:
library("ergm")
library("doParallel")
data("flo")
nflo <- network(flo, loops = TRUE)
fflo <- from.statnet(nflo)
## End(Not run)
Neighbors of an agent in a network
Description
Presents all neighbors of a given node.
Usage
get.neighbors(net, NodeID)
Arguments
net |
The input network. |
NodeID |
The ID of the input node. |
Details
Neighbors of a node are nodes that directly connects to this node.
Value
A vector.
Author(s)
Xu Dong
Examples
## Not run:
x <- net.ring.lattice(12,4)
get.neighbors(x,2)
## End(Not run)
Global Clustering Coefficient
Description
Calculate the global clustering coefficient of a graph.
Usage
metric.cluster.global(g)
Arguments
g |
The input network. |
Details
The global clustering coefficient measures the ratio of (closed) triples versus the total number of all possible triples in network g. metric.cluster.global() calculates the global clustering coefficient of g.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
References
Wasserman, Stanley, and Katherine Faust. Social network analysis: Methods and applications. Vol. 8. Cambridge university press, 1994.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
metric.cluster.global(x)
## End(Not run)
Mean Local Clustering Coefficient
Description
Calculate the average local clustering coefficient of a graph.
Usage
metric.cluster.mean(g)
Arguments
g |
The input network. |
Details
The local clustering coefficient of a node is the ratio of the triangles connected to the node and the triples centered on the node.metric.cluster.mean() calculates the (estimated) average clustering coefficient for all nodes in graph g with a justified error.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
References
Wasserman, Stanley, and Katherine Faust. Social network analysis: Methods and applications. Vol. 8. Cambridge university press, 1994.
Examples
## Not run:
x <- net.erdos.renyi.gnp(n = 1000, ncores = 3, p = 0.06)
metric.cluster.mean(x)
## End(Not run)
Median Local Clustering Coefficient
Description
Calculate the median local clustering coefficient of a graph.
Usage
metric.cluster.median(g)
Arguments
g |
The input network. |
Details
The local clustering coefficient of a node is the ratio of the triangles connected to the node and the triples centered on the node.metric.cluster.median() calculates the (estimated) median clustering coefficient for all nodes in graph g with a justified error.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
References
Wasserman, Stanley, and Katherine Faust. Social network analysis: Methods and applications. Vol. 8. Cambridge university press, 1994.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.1)
metric.cluster.median(x)
## End(Not run)
Effective Degree
Description
Calculate the effective degree of a network.
Usage
metric.degree.effective(g, effective_rate = 0.9)
Arguments
g |
The input network. |
effective_rate |
The effective rate (0.9 is set by default). |
Details
The effective degree
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
metric.degree.effective(x)
## End(Not run)
Degree Entropy
Description
Calculate the degree entropy of a graph.
Usage
metric.degree.entropy(g)
Arguments
g |
The input network. |
Details
Calculates the degree entropy of graph g, i.e.
Entropy(g) = - \sum_{i=1}^{n} i*\log _2(i)
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
References
Anand, Kartik, and Ginestra Bianconi. "Entropy measures for networks: Toward an information theory of complex topologies." Physical Review E 80, no. 4 (2009): 045102.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
metric.degree.entropy(x)
## End(Not run)
Maximal Degree
Description
Calculate the maximal degree of a graph.
Usage
metric.degree.max(g)
Arguments
g |
The input network. |
Details
The maximal degree.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
metric.degree.max(x)
## End(Not run)
Efficient Maximal Degree
Description
Calculate the efficient maximal degree of a graph.
Usage
metric.degree.max.efficient(g)
Arguments
g |
The input network. |
Details
The efficient maximal degree is the 90
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run: x <- net.erdos.renyi.gnp(1000, 0.01)
metric.degree.max.efficient(x)
## End(Not run)
Mean Degree
Description
Calculate the mean degree of a graph.
Usage
metric.degree.mean(g)
Arguments
g |
The input network. |
Details
The mean degree is the average value of the degrees of all nodes in graph g.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
metric.degree.mean(x)
## End(Not run)
Median Degree
Description
Calculate the median degree of a graph.
Usage
metric.degree.median(g)
Arguments
g |
The input network. |
Details
The median degree is the median value of the degrees of all nodes in graph g.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
metric.degree.median(x)
## End(Not run)
Minimal Degree
Description
Calculate the minimal degree of a network.
Usage
metric.degree.min(g)
Arguments
g |
The input network. |
Details
The minimal degree.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
metric.degree.min(x)
## End(Not run)
Standard Deviation of Degree Distribution
Description
Calculate the standard deviation of all degrees of a network.
Usage
metric.degree.sd(g)
Arguments
g |
The input network. |
Details
The standard deviation of all degrees of a network.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
metric.degree.sd(x)
## End(Not run)
Average Path Length
Description
Calculate the average path length of a graph.
Usage
metric.distance.apl(
Network,
probability = 0.95,
error = 0.03,
Cores = detectCores(),
full.apl = FALSE
)
Arguments
Network |
The input network. |
probability |
The confidence level probability. |
error |
The sampling error. |
Cores |
Number of cores to use in the computations. By default uses parallel function |
full.apl |
It will calculate the sampling version by default. If it is set to true, the population APL will be calculated and the rest of the parameters will be ignored. |
Details
The average path length (APL) is the average shortest path lengths of all
pairs of nodes in graph Network. metric.distance.apl calculates
the population APL and estimated APL of graph g with a sampling error set by the user.
The calculation uses a parallel load balancing approach, distributing jobs equally among the cores defined by the user.
Value
A real value.
Author(s)
Luis Castro, Nazrul Shaikh.
References
E. W. Dijkstra. 1959. A note on two problems in connexion with graphs. Numer. Math. 1, 1 (December 1959), 269-271.
Castro L, Shaikh N. Estimation of Average Path Lengths of Social Networks via Random Node Pair Sampling. Department of Industrial Engineering, University of Miami. 2016.
Examples
## Not run:
##Default function
x <- net.erdos.renyi.gnp(1000,0.01)
metric.distance.apl(x)
##Population APL
metric.distance.apl(x, full.apl=TRUE)
##Sampling at 99% level with an error of 10% using 5 cores
metric.distance.apl(Network = x, probability=0.99, error=0.1, Cores=5)
## End(Not run)
Diameter
Description
Calculate the diameter of a graph.
Usage
metric.distance.diameter(
Network,
probability = 0.95,
error = 0.03,
Cores = detectCores(),
full = TRUE
)
Arguments
Network |
The input network. |
probability |
The confidence level probability |
error |
The sampling error |
Cores |
Number of cores to use in the computations. By default uses parallel function |
full |
It will calculate the popular full version by default. If it is set to FALSE, the estimated diameter will be calculated. |
Details
The diameter is the largest shortest path lengths of all pairs of nodes in graph Network.
metric.distance.diameter calculates the (estimated) diameter
of graph Network with a justified error.
Value
A real value.
Author(s)
Luis Castro, Nazrul Shaikh.
References
E. W. Dijkstra. 1959. A note on two problems in connexion with graphs. Numer. Math. 1, 1 (December 1959), 269-271.
Castro L, Shaikh N. Estimation of Average Path Lengths of Social Networks via Random Node Pair Sampling. Department of Industrial Engineering, University of Miami. 2016.
Examples
## Not run:
##Default function
x <- net.erdos.renyi.gnp(1000,0.01)
metric.distance.diameter(x)
##Population APL
metric.distance.diameter(x, full=TRUE)
##Sampling at 99% level with an error of 10% using 5 cores
metric.distance.diameter(Network = x, probability=0.99, error=0.1, Cores=5)
## End(Not run)
Effective Diameter
Description
Calculate the effective diameter of a graph.
Usage
metric.distance.effdia(
Network,
probability = 0.95,
error = 0.03,
effective_rate = 0.9,
Cores = detectCores(),
full = TRUE
)
Arguments
Network |
The input network. |
probability |
The confidence level probability |
error |
The sampling error |
effective_rate |
The effective rate (by default it is set to be 0.9) |
Cores |
Number of cores to use in the computations. By default uses parallel function |
full |
It will calculate the popular full version by default. If it is set to FALSE, the estimated diameter will be calculated. |
Details
The diameter is the largest shortest path lengths of all pairs of nodes in
graph Network. metric.distance.diameter calculates the (estimated)
diameter of graph Network with a justified error.
Value
A real value.
Author(s)
Luis Castro, Nazrul Shaikh.
References
Dijkstra EW. A note on two problems in connexion with graphs:(numerische mathematik, _1 (1959), p 269-271). 1959.
Castro L, Shaikh N. Estimation of Average Path Lengths of Social Networks via Random Node Pair Sampling. Department of Industrial Engineering, University of Miami. 2016.
Examples
## Not run:
##Default function
x <- net.erdos.renyi.gnp(1000,0.01)
metric.distance.effdia(x)
##Population APL
metric.distance.effdia(x, full=TRUE)
##Sampling at 99% level with an error of 10% using 5 cores
metric.distance.effdia(Network = x, probability=0.99, error=0.1, Cores=5)
## End(Not run)
Mean Eccentricity
Description
Calculate the mean eccentricity of a graph.
Usage
metric.distance.meanecc(g, p)
Arguments
g |
The input network. |
p |
The sampling probability. |
Details
The mean eccentricities of all nodes in graph g. Calculates the (estimated) mean eccentricity of graph g with a justified error.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
References
West, Douglas Brent. Introduction to graph theory. Vol. 2. Upper Saddle River: Prentice Hall, 2001.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
metric.distance.meanecc(x, 0.01)
## End(Not run)
Median Eccentricity
Description
Calculate the (estimated) median eccentricity of a graph.
Usage
metric.distance.medianecc(g, p)
Arguments
g |
The input network. |
p |
The sampling probability. |
Details
Is the median eccentricities of all nodes in graph g.
metric.distance.medianecc calculates the (estimated) median eccentricity of graph g with a justified error.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
References
West, Douglas Brent. Introduction to graph theory. Vol. 2. Upper Saddle River: Prentice hall, 2001.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
metric.distance.medianecc(x, 0.01)
## End(Not run)
Median Path Length
Description
Calculate the median path length (MPL) of a network.
Usage
metric.distance.mpl(
Network,
probability = 0.95,
error = 0.03,
Cores = detectCores(),
full = FALSE
)
Arguments
Network |
The input network. |
probability |
The confidence level probability |
error |
The sampling error |
Cores |
Number of cores to use in the computations. By default |
full |
It calculates the sampling version by default. If it is set to true, the population MPL will be calculated and the rest of the parameters will be ignored. |
Details
The median path length (MPL) is the median shortest path lengths of all pairs of nodes in Network. metric.distance.mpl(g) calculates the population MPL OR estimated MPL of network g with a sampling error set by the user. The calculation uses a parallel load balancing approach, distributing jobs equally among the cores defined by the user.
Value
A real integer
Author(s)
Luis Castro, Nazrul Shaikh.
References
E. W. Dijkstra. 1959. A note on two problems in connexion with graphs. Numer. Math. 1, 1 (December 1959), 269-271.
Castro L, Shaikh N. Estimation of Average Path Lengths of Social Networks via Random Node Pair Sampling. Department of Industrial Engineering, University of Miami. 2016.
Examples
## Not run:
##Default function
x <- net.erdos.renyi.gnp(1000,0.01)
metric.distance.mpl(x)
##Population MPL
metric.distance.mpl(x, full=TRUE)
##Sampling at 99% level with an error of 10% using 5 cores
metric.distance.mpl(Network = x, probability=0.99, error=0.1, Cores=5)
## End(Not run)
Mean Eigenvalue Centrality
Description
Calculate the mean eigenvalue centrality of a graph.
Usage
metric.eigen.mean(g)
Arguments
g |
The input network. |
Details
metric.eigen.mean calculates the mean eigenvalue centrality score of graph g.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
References
Bonacich, Phillip, and Paulette Lloyd. "Eigenvector-like measures of centrality for asymmetric relations." Social networks 23, no. 3 (2001): 191-201.
Borgatti, Stephen P. "Centrality and network flow." Social networks 27, no. 1 (2005): 55-71.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
metric.eigen.mean(x)
## End(Not run)
Median Eigenvalue Centrality
Description
Calculate the median eigenvalue centrality of a graph.
Usage
metric.eigen.median(g)
Arguments
g |
The input network. |
Details
metric.eigen.median calculates the median eigenvalue centrality score of graph g.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
References
Bonacich, Phillip, and Paulette Lloyd. "Eigenvector-like measures of centrality for asymmetric relations." Social networks 23, no. 3 (2001): 191-201.
Borgatti, Stephen P. "Centrality and network flow." Social networks 27, no. 1 (2005): 55-71.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
metric.eigen.median(x)
## End(Not run)
Eigenvalue Score
Description
Calculate the eigenvalue centrality score of a graph.
Usage
metric.eigen.value(g)
Arguments
g |
The input network. |
Details
metric.eigen.value calculates the eigenvalue centrality score of graph g.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
References
Bonacich, Phillip, and Paulette Lloyd. "Eigenvector-like measures of centrality for asymmetric relations." Social networks 23, no. 3 (2001): 191-201.
Borgatti, Stephen P. "Centrality and network flow." Social networks 27, no. 1 (2005): 55-71.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
metric.eigen.value(x)
## End(Not run)
Graph Density
Description
Calculate the density of a graph.
Usage
metric.graph.density(g)
Arguments
g |
The input network. |
Details
Computes the ratio of the number of edges and the number of possible edges.
Value
A real constant.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
metric.graph.density(x)
## End(Not run)
Barabasi-Albert Scale-free Graph
Description
Simulate a scale-free network using a preferential attachment mechanism (Barabasi and Albert, 1999)
Usage
net.barabasi.albert(n, m, ncores = detectCores(), d = FALSE)
Arguments
n |
Number of nodes of the network. |
m |
Number of nodes to which a new node connects at each iteration. |
ncores |
Number of cores, by default |
d |
A logical value determining whether the generated network is a directed or undirected (default) network. |
Details
Starting with m nodes, the preferential attachment mechaism adds one node and m edges in each step. The edges will be placed with one end on the newly-added node and the other end on the existing nodes, according to probabilities that associate with their current degrees.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Luis Castro, Xu Dong, Nazrul Shaikh.
References
Barabasi, A.- L. and Albert R. 1999. Emergence of scaling in random networks. Science, 286 509-512.
Examples
## Not run:
x <- net.barabasi.albert(1000, 20) # using default ncores
## End(Not run)
Caveman Network
Description
Simulate a (connected) caveman network of m cliques of size k.
Usage
net.caveman(m, k, ncores = detectCores())
Arguments
m |
Number of cliques (or caves) in the network. |
k |
Number of nodes per clique. |
ncores |
Number of cores, by default |
Details
The (connected) caveman network is formed by connecting a set of isolated k - cliques
(or "caves"), neighbor by neighbor and head to toe, using one edge that removed from each
clique such that all m cliques form a single circle (Watts 1999). The total number of nodes, i.e. n, in this network is given by k*m.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Nazrul Shaikh.
References
Watts, D. J. Networks, Dynamics, and the Small-World Phenomenon. Amer. J. Soc. 105, 493-527, 1999.
Examples
## Not run:
x <- net.caveman(50, 20) #using ncores by default
## End(Not run)
Generate a cluster-affiliation graph
Description
Generate a cluster-affiliation graph.
Usage
net.cluster.affiliation(
DEG,
community_affiliation_alpha,
community_affiliation_lambda,
community_affiliation_min,
community_size_alpha,
community_size_lambda,
community_size_min
)
Arguments
DEG |
Degree sequence. |
community_affiliation_alpha |
First scaling parameter of the membership distribution. |
community_affiliation_lambda |
Second scaling parameter of the membership distribution. |
community_affiliation_min |
Minimal membership. |
community_size_alpha |
First scaling parameter of the cluster-size distribution. |
community_size_lambda |
Second scaling parameter of the cluster-size distribution. |
community_size_min |
Minimal size of a cluster. |
Details
The generated network has multiple (overlapping) densely-connected clusters.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Nazrul Shaikh
References
Dong X, Castro L, Shaikh N (2020). “fastnet: An R Package for Fast Simulation and Analysis of Large-Scale Social Networks.” Journal of Statistical Software, 96(7), 1-23. doi:10.18637/jss.v096.i07 (URL: https://doi.org/10.18637/jss.v096.i07)
Examples
## Not run:
DEG <- sample(seq(5,15),100, replace=TRUE)
x <- net.cluster.affiliation(DEG,
community_affiliation_alpha=1.5,
community_affiliation_lambda=10,
community_affiliation_min=1,
community_size_alpha=2.5,
community_size_lambda=40,
community_size_min=3)
## End(Not run)
Complete Network
Description
Simulate a complete (or full) network.
Usage
net.complete(n, ncores = detectCores())
Arguments
n |
Number of nodes of the network. |
ncores |
Number of cores, by default |
Details
The n nodes in the network are fully connected.
Note that the input n should not excess 10000, for the sake of memory overflow.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
x <- net.complete(1000) #using ncores by default
## End(Not run)
Generate a degree-constraint graph
Description
Generate a degree-constraint graph.
Usage
net.degree.constraint(DEG, c.alpha, c.min)
Arguments
DEG |
Degree sequence. |
c.alpha |
Scaling parameter of the community-size distribution. |
c.min |
Minimal size of a community. |
Details
The generated network has a pre-defined degree sequence with multiple (overlapping) communities.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Nazrul Shaikh
References
Dong X, Castro L, Shaikh N (2020). “fastnet: An R Package for Fast Simulation and Analysis of Large-Scale Social Networks.” Journal of Statistical Software, 96(7), 1-23. doi:10.18637/jss.v096.i07 (URL: https://doi.org/10.18637/jss.v096.i07)
Examples
## Not run:
DEG <- sample(seq(5,15),100, replace=TRUE)
x <- net.degree.constraint(DEG, c.alpha=2, c.min=3)
## End(Not run)
Directed / Undirected Erdos-Renyi G(n,m) network using a fix edge size.
Description
Simulate a random network with n nodes and m edges, according to Erdos and Renyi (1959).
Usage
net.erdos.renyi.gnm(n, m, ncores = detectCores(), d = TRUE)
Arguments
n |
Number of nodes of the network. |
m |
Number of edges of the network. |
ncores |
Number of cores, by default |
d |
A logical value determining whether is a network directed (default) or indirected. |
Details
In this (simplest) random network, m edges are formed at random among n nodes.
When d = TRUE is a directed network.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Nazrul Shaikh.
References
Erdos, P. and Renyi, A., On random graphs, Publicationes Mathematicae 6, 290-297 (1959).
Examples
## Not run:
x <- net.erdos.renyi.gnm(1000, 100)
## End(Not run)
Directed / Undirected Erdos-Renyi G(n,p) network
Description
Simulate a random network with n nodes and a link connecting probability of p, according to Edos and Renyi (1959).
Usage
net.erdos.renyi.gnp(n, p, ncores = detectCores(), d = TRUE)
Arguments
n |
Number of nodes of the network. |
p |
Connecting probability. |
ncores |
Number of cores, by default |
d |
A logical value determining whether is a network directed (default) or indirected. |
Details
In this (simplest) random network, each edge is formed at random with a constant probability.
When d = TRUE is a directed network.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Luis Castro, Xu Dong, Nazrul Shaikh.
References
Erdos, P. and Renyi, A., On random graphs, Publicationes Mathematicae 6, 290-297 (1959).
Examples
## Not run:
x <- net.erdos.renyi.gnp(1000, 0.01)
## End(Not run)
Holme-Kim Network
Description
Simulate a scale-free network with relatively high clustering, comparing to B-A networks (Holme and Kim, 1999).
Usage
net.holme.kim(n, m, pt)
Arguments
n |
Number of nodes of the network. |
m |
Number of nodes to which a new node connects at each iteration. |
pt |
Triad formation probability after each preferential attachment mechanism. |
Details
The Holme-Kim network model is a simple extension of B-A model. It adds an additional step, called "Triad formation", with the probability pt that compensates the low clustering in B-A networks.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Nazrul Shaikh
References
Holme, Petter, and Beom Jun Kim. "Growing scale-free networks with tunable clustering."Physical review E65, no. 2 (2002): 026107.
Examples
## Not run:
x <- net.holme.kim (1000, 20, 0.1)
## End(Not run)
Random Network with a Power-law Degree Distribution that Has An Exponential Cutoff
Description
Simulate a random network with a power-law degree distribution that has an exponential cutoff, according to Newman et al. (2001).
Usage
net.random.plc(n, cutoff, exponent)
Arguments
n |
The number of the nodes in the network. |
cutoff |
Exponential cutoff of the degree distribution of the network. |
exponent |
Exponent of the degree distribution of the network. |
Details
The generated random network has a power-law degree distribution with an exponential degree cutoff.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Nazrul Shaikh
References
Newman, Mark EJ, Steven H. Strogatz, and Duncan J. Watts. "Random graphs with arbitrary degree distributions and their applications." Physical review E 64, no. 2 (2001): 026118.
Examples
## Not run:
x <- net.random.plc(1000, 10, 2)
## End(Not run)
Rewired (Connected) Caveman Network
Description
Simulate a rewired caveman network of m cliques of size k, and with a link rewiring probability p.
Usage
net.rewired.caveman(nc, m, p, seed = 99)
Arguments
nc |
Number of cliques (or caves) in the network. |
m |
Number of nodes per clique. |
p |
Link rewiring probability. |
seed |
A random seed. |
Details
The rewired caveman network is built on the corresponding regular caveman network with m cliques of size k. Then the links in this caveman network are rewired with probability p.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Nazrul Shaikh
References
Watts, D. J. Networks, Dynamics, and the Small-World Phenomenon. Amer. J. Soc. 105, 493-527, 1999.
Examples
## Not run:
x <- net.rewired.caveman(50, 20, 0.0005)
## End(Not run)
k - regular ring lattice
Description
Simulate a network with a k -regular ring lattice structure.
Usage
net.ring.lattice(n, k)
Arguments
n |
Number of nodes in the network. |
k |
Number of edges per node. |
Details
The n nodes are placed on a circle and each node is connected to the nearest k neighbors.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Nazrul Shaikh
References
Duncan J Watts and Steven H Strogatz: Collective dynamics of 'small world' networks, Nature 393, 440-442, 1998.
Examples
## Not run:
x <- net.ring.lattice(1000, 10)
## End(Not run)
Watts-Strogatz Small-world Network
Description
Simulate a small-world network according to the model of Watts and Strogatz (1998).
Usage
net.watts.strogatz(n, k, re)
Arguments
n |
The number of the nodes in the network (or lattice). |
k |
Number of edges per node. |
re |
Rewiring probability. |
Details
The formation of Watts-Strogatz network starts with a ring lattice with n nodes and k edges per node, then each edge is rewired at random with probability re.
Value
A list containing the nodes of the network and their respective neighbors.
Author(s)
Xu Dong, Nazrul Shaikh
References
Duncan J. Watts and Steven H. Strogatz: Collective dynamics of 'small world' networks, Nature 393, 440-442, 1998.
Examples
## Not run:
x <- net.watts.strogatz(1000, 10, 0.05)
## End(Not run)
Preview of the degree distribution of a network
Description
Present the first 10 degrees of a network.
Usage
preview.deg(g)
Arguments
g |
The input network. |
Details
Present the first 10 degrees of a network.
Value
A vector.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
x <- net.ring.lattice(12,4)
preview.deg(x)
## End(Not run)
Preview of a network
Description
Present the first 10 ego-centric lists of a network.
Usage
preview.net(net)
Arguments
net |
The input network. |
Details
the connection condition of the first 10 nodes in a network.
Value
A list.
Author(s)
Xu Dong, Nazrul Shaikh.
Examples
## Not run:
x <- net.ring.lattice(12,4)
preview.net(x)
## End(Not run)
fastnet to edgelist
Description
Coerce a fastnet object to edgelist.
Usage
to.edgelist(network, ncores)
Arguments
network |
A fastnet object. |
ncores |
The number of cores to be used. |
Value
A 2-column list with each row representing an edge, from source to destination
Author(s)
Xu Dong
Examples
## Not run:
g <- net.erdos.renyi.gnp(100, 0.1)
el <- to.edgelist(g)
## End(Not run)
fastnet to igraph
Description
Coerce a fastnet object to an igraph object
Usage
to.igraph(g)
Arguments
g |
A fastnet object |
Value
An igraph object
Author(s)
Xu Dong
fastnet to tidygraph
Description
Coerce a fastnet object to a tidygraph object.
Usage
to.tidygraph(g)
Arguments
g |
A fastnet object. |
Value
A tidygraph object
Author(s)
Xu Dong