tnet (version 2.6)

tnet-package: Collection of functions for analysing weighted networks, two-mode networks, and longitudinal networks

Description

This package can analyse weighted networks, two-mode networks, and longitudinal networks. Binary ties limit the richness of networks as all relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties depend on each other.

Details

ll{ Package: tnet Type: Package Version: 2.6 Date: 2010-04-17 } This package allows for analysis of weighted network, two-mode network, and longitudinal network datasets. More information is available on http://opsahl.co.uk/tnet/ and http://toreopsahl.com It utlises three forms of data structures (it can automatically convert matrices etc into these formats, see the as.tnet-function): 1) simple weighted data in the following format (vertex.id.of.creator vertex.id.of.target weight.of.tie): 1 2 4 1 3 2 2) two-mode data in the following format (vertex.id.of.node.in.set.1 vertex.id.of.node.in.set.2 optional.weight.of.tie): 1 1 1 2 1 2 3) timed data in the following format (MySQL-timestamp.surrounded.by.quotes vertex.id.of.creator vertex.id.of.target weight): "2007-09-12 13:45:00" 1 2 1 "2007-09-12 13:46:31" 1 2 1 If ties are repeated, the tie increases the weighted. The weight column decides how much weight is added at each time (this can take a negative value to decrease the weight). Attribute files are read as follows: 0 1 3 0 3 2 1 3 3 where the first row refers to vertex id 1, the second row to vertex id 2, and so on. The first column refers to the first attributes, second column to the second attribute and so on.

References

http://opsahl.co.uk/tnet/

Examples

# Generate a random weighted graph
rg <- rg_w(nodes=100,arcs=300,directed=TRUE)

# Calculate clustering coefficient
clustering_w(rg)