Generate Barabasi-Albert Graphs #105
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added 26 commits
April 19, 2018 14:23
Instead of degree, which then had to be divided by two. That required making sure we have an even number, which now is not neccessary anymore.
The function takes the number of nodes, a probability and a seed and returns a vector of additional nodes as described in Newman and Watts (1999).
Functional composition of gen-circle and add-shortcuts.
So far only the clustering coefficient for a single node can be computed.
As described in Watts and Strogatz (1998). This is really the average over all clustering coefficients.
Because of this, the function is not variadic anymore.
This is the preferential attachment graph. Remove to old initializing function and start with two connected nodes, to which a new one will attach with equal probability. First test checks for the number of nodes created and passes.
Either explicitly pass a random seed when creating the graph, or use System/nanoTime.
Test if function actually constructs a loom graph and for number of nodes and edges.
Sometimes Node or Edge count are lower than expected. This is because the construction of the graph involves probabilistic decisions. Because of this, test if actual number is bigger than some percentage of the expected count.
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This adds a function to generate small-world graphs as proposed by Barabasi and Albert (1999), also know as preferential attachment graphs or power law graphs, as well as some tests. Please let me know, if you see any problems with the code.