I am interested about plotting graphs of a phenomenon and study it using tools from social network analysis.
Suppose the nodes are time series, and that the links between the nodes are the correlation between them. The first issue I have is how to compute the correlation between the nodes. For instance, between two nodes (i.e., between two time series) there may be a lag-0 correlation term, and/or some other lag-n correlation coefficients. Is there a way to get one single number from this? After all, I only want to have one link between two nodes, as multiple links would make the analysis more difficult.
Has any work been done in the literature in this sense, i.e., studying correlation of time series from a social network analysis perspective?
Best Answer
Some literature is available on Tensor analysis, which might be of relevance in such kind of problems. Search for some papers on- 'Tensor analysis on dynamic graphs'. Introduction to these methods- http://www.graphanalysis.org/SIAM-PP08/Dunlavy.pdf
There have been few recent approaches using Ising model and Gibbs sampling as well to handle such type of data. Some guys from Carnegie are doing work in this directions. Tools/code is open sourced.