StochasticBlockModel.jl is a Julia package that provides methods for common tasks related to Stochastic Block Models (SBM), such as generating random graphs with community structure and detecting communities in a given network. Community detection is performed by estimating the parameters of a SBM from an observed graph. Different heuristics and one exact method are provided for the problem of finding the maximum log-likelihood estimate of the parameters of a SBM.
To install this package, open the Julia REPL and type ]add StochasticBlockModel.
Generating a graph with community structure:
julia> using StochasticBlockModel
julia> w = [1.0 0.2;
0.2 1.0]
julia> n_per_community = [6; 6]
julia> dataset = generate(w, n_per_community, seed=0)
julia> dataset.A
12×12 Array{Int64,2}:
2 2 0 0 1 1 0 0 0 0 0 0
2 0 0 0 1 1 0 0 0 0 0 0
0 0 0 0 0 2 0 1 0 1 0 0
0 0 0 4 2 3 0 1 1 0 0 0
1 1 0 2 0 2 0 0 1 0 0 0
1 1 2 3 2 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 2 1 1
0 0 1 1 0 0 0 2 0 1 1 1
0 0 0 1 1 0 0 0 0 1 2 0
0 0 1 0 0 0 2 1 1 2 0 1
0 0 0 0 0 0 1 1 2 0 4 1
0 0 0 0 0 0 1 1 0 1 1 0Detecting communities in a graph:
julia> using StochasticBlockModel
julia> dataset = Dataset("../instances/zachary.in")
julia> sbm, x, opt_results = estimate(dataset, time_limit=10.0)
julia> sbm.w
2×2 Array{Float64,2}:
0.551446 1.59584
1.59584 0.20851
julia> StochasticBlockModel.displayResults(opt_results)
--------------- Opt Results ---------------
Obj. LB = -Inf
Obj. UB = 62.52483
Status: LocalOptimum_LS1
Solve time: 0.016 seconds
Iterations: 2