Python implementation of some algorithms for Correlation Clustering. Specifically:

• Linear-programming + region-growing O(log n)-approximation algorithms for general weighted graphs
  • round_demaine in src/pyccalg.py: Demaine et al.'s rounding algorithm
  • round_charikar in src/pyccalg.py: Charikar et al.'s rounding algorithm
• kwikcluster in src/pyccalg.py: KwikCluster randomized, linear-time algorithm (Ailon et al., JACM 2008), achieving constant-factor approximation guarantees on complete graphs satisfying certain constraints (e.g., probability constraint and/or triangle-inequality constraint)

• Python v3.6+
• For linear-programming-based algorithms:
  • SciPyv1.6+ and/or PuLP
  • SciPy linprog comes with various solvers: 'Method highs-ds is a wrapper of the C++ high performance dual revised simplex implementation (HSOL). Method highs-ipm is a wrapper of a C++ implementation of an interior-point method; it features a crossover routine, so it is as accurate as a simplex solver. Method highs chooses between the two automatically. For new code involving linprog, we recommend explicitly choosing one of these three method values instead of interior-point (default), revised simplex, and simplex (legacy)'. See here for more details.
  • PuLP comes with two solvers by default: CBC (linear and integer programming) and CHOCO (constraint programming), but it can connect to many others (e.g., GUROBI, CPLEX, SCIP, MIPCL, XPRESS, GLPK9) if you have them installed
  • Here we use highs-ipm with SciPy linprog and the default CBC with PuLP
  • However, any linear-programming Python (other than SciPy linprog or PuLP) library can alternatively be used with minimal adaption

python src/pyccalg.py -d <DATASET_FILE> [-r <LB,UB>] [-a <PROB>] [-s {'pulp','scipy'}] [-m {'charikar','demaine','kwik'}]

• Optional arguments:
  • -r <LB,UB>, if you want to generate random edge weights from [LB,UB] range
  • -a <PROB>, if you want to randomly add edges with probability PROB
  • -m {'charikar','demaine','kwik'}, to choose the algorithm (default: 'charikar'). NOTE: kwikcluster is always run too
  • -s {'pulp','scipy'}, to select the solver to be used (default: 'scipy' (it seems faster))
• Dataset-file format:
  • First line: #VERTICES \t #EDGES
  • One line per edge; every line is a quadruple: NODE1 \t NODE2 \t POSITIVE_WEIGHT \t NEGATIVE_WEIGHT (POSITIVE_WEIGHT and NEGATIVE_WEIGHT are ignored if code is run with -r option)
  • Look at data folder for some examples