getAdjacencyMatrix: A function that extracts valuable properties from a raw...

In netcmc: Spatio-Network Generalised Linear Mixed Models for Areal Unit and Network Data

A function that extracts valuable properties from a raw social network.

Description

This function transforms a network, which is a data.frame type in a specified format, in to a resultant n by n adjacency matrix, where a_{ij} = 0 if vertex i and j (i \neq j) are not adjacent i.e. vertex i and j are not the head/tail of an edge e and a_{ij} = 1 if vertex i and j (i \neq j) are adjacent i.e. vertex i and j are the head/tail of an edge e. a_{ij} = 0 when i = j.

Usage

getAdjacencyMatrix(rawNetwork)

Arguments

rawNetwork

The data.frame which encodes information about the network. The dimensions of the matrix are n by (l+1).The data.frame contains one column corresponding to the labels for each of the n vertices in the network, the column name for this should be ‘labels’. The other l columns corresponds to the corresponds to the vertices which are adjacent to each of the n vertices in the network. It is important to note that the label of a vertex should not be 0. The nth vertex can be adjacent to a maximum of l other vertices.

Value

adjacencyMatrix	The resultant adjaceny matrix for the rawNetwork data.frame.
nonnominators	The individuals in the social network who are nominees of at least one other individual but were not in the set of individuals who did the nominating.
vertexNoOutdegrees	The individuals in the social network that have an outdegree of 0.
vertexNoIndegrees	The individuals in the social network that have an indegree of 0.
vertexIsolates	The individuals in the social network that have an outdegree and indegree of 0.

Author(s)

George Gerogiannis

Examples

rawNetwork = matrix(NA, 4, 3)
rawNetwork = as.data.frame(rawNetwork)
colnames(rawNetwork)[1] = "labels"
rawNetwork[, 1] = c("A", "B", "C", "D")
rawNetwork[, 2] = c(0, "C", "D", 0)
rawNetwork[, 3] = c("B", 0, "A", "C")
getAdjacencyMatrix(rawNetwork)

rawNetwork = matrix(NA, 4, 3)
rawNetwork = as.data.frame(rawNetwork)
colnames(rawNetwork)[2] = "labels"
rawNetwork[, 1] = c(NA, "Charlie", "David", 0)
rawNetwork[, 2] = c("Alistar", "Bob", "Charlie", "David")
rawNetwork[, 3] = c("Bob", NA, "Alistar", "Charlie")
getAdjacencyMatrix(rawNetwork)

rawNetwork = matrix(NA, 4, 3)
rawNetwork = as.data.frame(rawNetwork)
colnames(rawNetwork)[1] = "labels"
rawNetwork[, 1] = c(245, 344, 234, 104)
rawNetwork[, 2] = c(NA, 234, 104, NA)
rawNetwork[, 3] = c(344, 0, 245, 234)
getAdjacencyMatrix(rawNetwork)

rawNetwork = matrix(NA, 4, 3)
rawNetwork = as.data.frame(rawNetwork)
colnames(rawNetwork)[1] = "labels"
rawNetwork[, 1] = c(245, 344, 234, 104)
rawNetwork[, 2] = c(32, 234, 104, 0)
rawNetwork[, 3] = c(344, 20, 245, 234)
getAdjacencyMatrix(rawNetwork)

rawNetwork = matrix(NA, 4, 3)
rawNetwork = as.data.frame(rawNetwork)
colnames(rawNetwork)[1] = "labels"
rawNetwork[, 1] = c("Alistar", "Bob", "Charlie", "David")
rawNetwork[, 2] = c(NA, "Charlie", "David", 0)
rawNetwork[, 3] = c("Bob", "Blaine", "Alistar", "Charlie")
getAdjacencyMatrix(rawNetwork)

rawNetwork = matrix(NA, 4, 3)
rawNetwork = as.data.frame(rawNetwork)
colnames(rawNetwork)[1] = "labels"
rawNetwork[, 1] = c("Alistar", "Bob", "Charlie", "David")
rawNetwork[, 2] = c(0, "Charlie", 0, 0)
rawNetwork[, 3] = c("Bob", "Blaine", "Alistar", 0)
getAdjacencyMatrix(rawNetwork)

rawNetwork = matrix(NA, 4, 3)
rawNetwork = as.data.frame(rawNetwork)
colnames(rawNetwork)[1] = "labels"
rawNetwork[, 1] = c(245, 344, 234, 104)
rawNetwork[, 2] = c(32, 0, 104, 0)
rawNetwork[, 3] = c(34, 0, 245, 234)
getAdjacencyMatrix(rawNetwork)

netcmc documentation built on Nov. 10, 2022, 5:11 p.m.