R：具有（负）权重的有向图上的社区检测

Question

I am analysing a directed, weighted network with R igraph. The network is based on a correlation matrix, ie weights go from -1 to +1. This network is clearly undirected, but I am also interested in more general cases.

Based on this network I would like to perform a community detection to group "similar" nodes together. I know there is a whole bunch of community detection methods in R igraph. See for example here or here .

But none of these cases deals with negative weights.

Is there an implementation in igraph (or in some other R package) which can deal with directed networks which have negative weights? Any hints are very appreciated.

Answer 1

Not 100 % sure, if it violates any assumptions, but as a workaround I set all negative edge weights to zero before calculating Louvain community detection with igraph in R. At least, they are never included in community relationships.

E(g)$width <- ifelse(E(g)$width < 0, 0, E(g)$width)
g.louv <- cluster_louvain(g, weights = E(g)$width)

Note: this applies only to undirected graphs (I overlooked this detail of the question, sorry)

Answer 2

Addressing a more general issue. Using graphs and community detection to learn about groups of variables that have higher correlations with variables within the community rather than outside of the community seems rather exotic to me. Analyzing correlation matrices for groups of strongly correlated variables is a well known problem in statistics since 70+ years. Why don't you use PCA (eigenvalue decomposition) or SVD or MDS or some column clustering? All can be easily performed in R.