from evaluations import parameters from evaluations.run_experiment import run_experiment """ Experiment: Determine the Influence of the Sybil Node Classifier on Sybilframe System: Votetrust Targeted Breadth First Set of variations: FN = FP: 0.1, 0.2, 0.4 Yes, large node priors indicate BENIGN labels """ evalIntervals = (1,5,10,15,20,25,30,35,40,45,50) graph = 'slashdot' for i in (0.1, 0.3, 0.6, 0.8): paras = parameters.ParameterSettingsP(graph=graph, strategy='breadthFirst', boosted=False, evalAt=evalIntervals, numRepeats=1) paras.nodeProbSybil = 1-i paras.nodeProbNonSybil = 0.1 paras.edgeProbSybil = 0.8 paras.edgeProbNonSybil = 0.2 paras.numSeeds = 100 run_experiment(paras, saveAs='./sybilNodeProb/sybilNodeProb{}PTar{}.p'.format(i, graph), systems=('sybilframe',))
from evaluations import parameters from evaluations.run_experiment import run_experiment """ Experiment: Determine the Influence of the Victim Classifier's performance on the performance of Integro System: Votetrust Targeted Breadth First Set of variations: d=0.8, 0.9, 0.95, 0.99, 0.99 """ graph = 'slashdot' numRepeats = 5 evalInt = (10, 20, 40) for i in (0.1, 0.2, 0.4): paras = parameters.ParameterSettingsSR(graph=graph, strategy='breadthFirst', evalAt=evalInt, numRepeats=numRepeats) paras.nodeProbNonVictim = 0.9 paras.nodeProbVictim = i run_experiment(paras, saveAs='./victimProb/victimProb{}{}SRTar.p'.format(i, graph), systems=('integro',)) paras = parameters.ParameterSettingsP(graph=graph, strategy='breadthFirst', boosted=False, evalAt=evalInt, numRepeats=numRepeats) paras.nodeProbNonVictim = 0.9 paras.nodeProbVictim = i run_experiment(paras, saveAs='./victimProb/victimProb{}{}PTar.p'.format(i, graph), systems=('integro',))
Variations: Number of seeds: 1, 3, 10, 100, 1000 Systems: All Systems, all scenarios """ for i in (1, 3, 10, 100, 1000): paras = parameters.ParameterSettingsP(graph='facebook', strategy='breadthFirst', boosted=True, evalAt=(50, ), numRepeats=3) paras.numSeeds = i run_experiment(paras, saveAs='./seeds/seeds{}PTar.p'.format(i)) paras = parameters.ParameterSettingsP(graph='facebook', strategy='random', boosted=False, evalAt=(50, ), numRepeats=3) paras.numSeeds = i run_experiment(paras, saveAs='./seeds/seeds{}PRand.p'.format(i)) paras = parameters.ParameterSettingsSR(graph='facebook', evalAt=(50, ), numRepeats=3) paras.numSeeds = i run_experiment(paras, saveAs='./seeds/seeds{}SRRand.p'.format(i))
Determine the success rate influences the performance Variations: Success Rate: 0.2 - 0.8 0.1 - 0.5 Systems: All Systems, all scenarios """ graph = 'slashdot' for i in ((0.2, 0.7), (0.1, 0.5)): paras = parameters.ParameterSettingsP(graph=graph, strategy='breadthFirst', boosted="random", evalAt=(50, ), numRepeats=3) paras.acceptanceRatioLimits = i run_experiment(paras, saveAs='./ratio/ratio{}PTar{}.p'.format(i, graph)) """ paras = parameters.ParameterSettingsP(graph=graph, strategy='random', boosted=False, evalAt=(50,), numRepeats=3) paras.acceptanceRatioLimits = i run_experiment(paras, saveAs='./ratio/ratio{}PRand.p'.format(i)) paras = parameters.ParameterSettingsSR(graph=graph, evalAt=(50,), numRepeats=3) paras.acceptanceRatioLimits = i run_experiment(paras, saveAs='./ratio/ratio{}SRRand.p'.format(i)) """
Votetrust Targeted Breadth First Set of variations: FP: 0.1, FN: 0.1, 0.3, 0.5, 0.6 """ graph = 'facebook' for i in (0.1, 0.3, 0.6, 0.81): for j in (0.1, 0.32, 0.6, 0.8): paras = parameters.ParameterSettingsP(graph=graph, strategy='breadthFirst', boosted=False, evalAt=(50, ), numRepeats=3) paras.edgeProbSybil = 1 - i paras.edgeProbNonSybil = 0.1 paras.nodeProbSybil = 1 - j paras.nodeProbNonSybil = 0.1 paras.numSeeds = 100 run_experiment( paras, saveAs='./sybilProb/sybilProb{}PTar{}_node{}_edge{}.p'.format( i, graph, str(round(1 - paras.nodeProbSybil, 2))[2:], str(round(1 - paras.edgeProbSybil, 2))[2:]), systems=('sybilframe', ))
from evaluations import parameters from evaluations.run_experiment import run_experiment """ Experiment: Determine the Influence of the D parameter on the performance of Votetrust System: Votetrust Targeted Breadth First Set of variations: d=0.8, 0.9, 0.95, 0.99, 0.999 """ boosttype = 'random' graph = 'facebook' for i in (0.8, 0.99, 0.999): paras = parameters.ParameterSettingsP(graph=graph, strategy='breadthFirst', boosted=boosttype, evalAt=(50, ), numRepeats=5) paras.d = i paras.numSeeds = 100 run_experiment(paras, saveAs='./d/d{}PTar_{}_{}.p'.format(i, boosttype, graph), systems=('votetrust', ))