Graph mining and generators
Simple examples on algorithms typically used in graph mining (work in progress).
Introduction
This project welcomes open collaboration and feedback.
Graph Mining
Visualization
Using tkplot to visualize a simple graph on macOS isn’t the best because I keep a pop-up window that I don’t like one bit.
library(intergraph)
library(igraph)
# note that the graphs converted to dimacs format can be read directly like this
g <- read.graph("INDDGO/sample_graphs/WikiExample.graph", format = "dimacs",directed=F)
Coord <- tkplot(g, vertex.size=3, vertex.label=V(g)$role,vertex.color='darkgreen')
MCoords <- tkplot.getcoords(Coord)
plot(g, layout=MCoords, vertex.size=8, vertex.label=NA, vertex.color="lightblue")
Marginalization in general graphs
The marginal distribution of a subset of a collection of random variables is the prob. distribution of the variables contained in the subset.