`nwcorrelate`

Correlate networks and variables

Syntax

1) Correlate nodes of a network
		
nwcorrelate 
netname
[,
context(context)
name(newnetname)]
     2) Correlate two networks with each other
nwcorrelate 
netname1
netname2
[,
permutations(int)
save(filename)
kdensity_options]
     3) Correlate one network and one variable
		
nwcorrelate 
netname
, 
attribute(varname)
[mode(expand_mode)
name(newnetname)
keepexpand
permutations(int)
save(filename)
kdensity_options]


`context`(context)	Determines whether incoming or outgoing ties should be considered when correlating two nodes; default = both
`name(newnetname)`	Name of new network with node correlations; default = _corr
`mode`(expand_mode)	How to expand the attribute variable
`keepexpand`	Keep the network generated by nwexpand
`permutations(integer)`	Number of QAP permuations
`save(filename)`	Save QAP permuation results in file

Description

This command is the network version of correlate. It can be used in three different ways:

1) Correlate nodes of a network

2) Correlate two networks with each other

3) Correlate one network and one variable

The option permutation() creates QAP permutations of the first network and generates a distribution of correlation coefficients under the null-hypothesis that there is no correlation. In practice, rows and columns of netname are reshuffled and the correlation coefficient is calculated again and again. Based on this distribution a p-value and a confidence interval is calculated. A plot is displayed and additional information is returned in the return vector.

Supported network types

Binary: yes. Directed: yes, via context(incoming|outgoing|both) for the node-correlation mode. Weighted: yes, natively - uses raw tie values throughout, no dichotomize option. Signed: not checked. Two-mode: not checked.

Stored results

Node correlations

Scalars:
r(avg_corr) average correlation coefficient between nodes
Macros:
r(name) name of network
r(corrname) name of new network with coefficients

Two networks or one network and one attribute

Scalars:
r(corr) correlation coefficient
r(pvalue) p-value of correlation coefficient
r(ub) upper bound, 95% confidence interval
r(lb) lower bound, 95% confidence interval
Macros:
r(name_1) name of netname1
r(name_2) name of netname2 or the expanded network

References

Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology 78(6), 1360-1380.

Zachary, W. (1977). An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452-473.

nwcorrelate