Test Graph Connectivity MaliciousNodes vs NetDegree & MaliciouNodes vs NoOfNodes

.gitignore

@@ -5,3 +5,4 @@ myenv*/
 doc/_build
 !results/plots.py
 Frontend/
+ConnectivityTest/*/results/*

ConnectivityTest/MaliciousNodesVsNetDegree/prog.py

@@ -0,0 +1,119 @@
+import networkx as nx
+import random
+import matplotlib.pyplot as plt
+import sys
+from datetime import datetime
+import os
+
+
+def plotData(conf):
+    for key, value in conf['y'].items():
+        plt.plot(conf['x'], value, label=f"{conf['label']}: {key}")
+    plt.xlabel(conf['xlabel'])
+    plt.ylabel(conf['ylabel'])
+    plt.title(conf['title'])
+    plt.legend()
+    plt.savefig(conf['plotPath'])
+    plt.clf()
+
+def isGConnected(deg, nodes, mal):
+    G = nx.random_regular_graph(deg, nodes)
+    malNodes = random.sample(list(G.nodes()), k=mal * nodes // 100)
+    for mn in malNodes:
+        G.remove_node(mn)
+    
+    return nx.is_connected(G)
+
+def getValidatorCountPerColumn(numberOfCols, numOfValidators, chiC):
+    validatorCountPerColumn = dict()
+    for _ in range(numOfValidators):
+        colsSelected = random.sample(list(range(1, numberOfCols + 1)), chiC)
+        for col in colsSelected:
+            if col in validatorCountPerColumn.keys():
+                validatorCountPerColumn[col] += 1
+            else:
+                validatorCountPerColumn[col] = 0
+    
+    return validatorCountPerColumn
+
+def runOnce(run_i, runs, deg, validatorCountPerCol, malNodesPercentage):
+    print(f"Running: {run_i + 1} / {runs}")
+    
+    isParted = False
+    partCount = 0
+    isPartedCount = 0
+    for col in validatorCountPerCol.keys():
+        nodes = validatorCountPerCol[col]
+        if not isGConnected(deg, nodes, malNodesPercentage):
+            if not isParted: isParted = True
+            partCount += 1
+    
+    if isParted: isPartedCount += 1
+    sys.stdout.write("\033[F")
+    
+    return isPartedCount, partCount
+
+def study():
+    degPartPercentages = dict()
+    degAvgDisconnectedCols = dict()
+    
+    for deg in degs:
+        print(f"\nNetwork Degree: {deg}")
+        
+        partPercentages = list()
+        avgDisconnectedCols = list()
+        for mal in mals:
+            isPartedCount = partCount = 0
+            validatorCountPerColumn = getValidatorCountPerColumn(numberOfColumns, numberOfValidators, custody)
+            for _run in range(runs):
+                _isPartedCount, _partCount = runOnce(_run, runs, deg, validatorCountPerColumn, mal)
+                isPartedCount += _isPartedCount
+                partCount += _partCount
+            
+            partPercentages.append(isPartedCount * 100 / runs)
+            avgDisconnectedCols.append(partCount / runs)
+            print(f"Malicious Nodes: {mal}%, Partition Percentage: {partPercentages[-1]}, Avg. Partitions: {avgDisconnectedCols[-1]}")
+        
+        degPartPercentages[deg] = partPercentages
+        degAvgDisconnectedCols[deg] = avgDisconnectedCols
+    
+    now = datetime.now()
+    execID = now.strftime("%Y-%m-%d_%H-%M-%S_")+str(random.randint(100,999))
+    newpath = f"ConnectivityTest/MaliciousNodesVsNetDegree/results/{execID}/"
+    if not os.path.exists(newpath): os.makedirs(newpath)
+    
+    conf1 = {
+        'x': mals,
+        'y': degPartPercentages,
+        'label': "NW Deg",
+        'xlabel': "Malicious Node (%)",
+        'ylabel': "Partition Possibility (%)",
+        "title": "Possibility of Network Graph Get Partitioned for Malicious Nodes",
+        "plotPath": f"{newpath}prob.png"
+    }
+    
+    conf2 = {
+        'x': mals,
+        'y': degAvgDisconnectedCols,
+        'label': "NW Deg",
+        'xlabel': "Malicious Node (%)",
+        'ylabel': "Avg. Disconnected Columns",
+        "title": "Malicious Nodes (%) vs. Disconnected Columns",
+        "plotPath": f"{newpath}num.png"
+    }
+    
+    plotData(conf1)
+    plotData(conf2)
+
+
+# Configuration
+runs = 10
+degs = range(6, 9, 2)
+mals = range(5, 100, 5)
+numberOfColumns = 128
+custody = 4
+numberOfNodes = 5000
+numberOfValidators = int(numberOfNodes * 2.4) # (0.8 * 1 + 0.2 * 8 = 2.4)
+
+if __name__ == "__main__":
+    study()

ConnectivityTest/MaliciousNodesVsNumberOfNodes/prog.py

@@ -0,0 +1,120 @@
+import networkx as nx
+import random
+import matplotlib.pyplot as plt
+import sys
+import numpy as np
+from datetime import datetime
+import os
+
+
+def plotData(conf):
+    for key, value in conf['y'].items():
+        plt.plot(conf['x'], value, label=f"{conf['label']}: {key}")
+    plt.xlabel(conf['xlabel'])
+    plt.ylabel(conf['ylabel'])
+    plt.title(conf['title'])
+    plt.legend()
+    plt.savefig(conf['plotPath'])
+    plt.clf()
+
+def isGConnected(deg, nodes, mal):
+    G = nx.random_regular_graph(deg, nodes)
+    malNodes = random.sample(list(G.nodes()), k=mal * nodes // 100)
+    for mn in malNodes:
+        G.remove_node(mn)
+    
+    return nx.is_connected(G)
+
+def getValidatorCountPerColumn(numberOfCols, numOfValidators, chiC):
+    validatorCountPerColumn = dict()
+    for _ in range(numOfValidators):
+        colsSelected = random.sample(list(range(1, numberOfCols + 1)), chiC)
+        for col in colsSelected:
+            if col in validatorCountPerColumn.keys():
+                validatorCountPerColumn[col] += 1
+            else:
+                validatorCountPerColumn[col] = 0
+    
+    return validatorCountPerColumn
+
+def runOnce(run_i, runs, deg, validatorCountPerCol, malNodesPercentage):
+    print(f"Running: {run_i + 1} / {runs}")
+    
+    isParted = False
+    partCount = 0
+    isPartedCount = 0
+    for col in validatorCountPerCol.keys():
+        nodes = validatorCountPerCol[col]
+        if not isGConnected(deg, nodes, malNodesPercentage):
+            if not isParted: isParted = True
+            partCount += 1
+    
+    if isParted: isPartedCount += 1
+    sys.stdout.write("\033[F")
+    
+    return isPartedCount, partCount
+
+def study():
+    nnPartPercentages = dict()
+    nnAvgDisconnectedCols = dict()
+    
+    for nn, nv in zip(numberOfNodes, numberOfValidators):
+        print(f"\nNumber of Nodes: {nn}")
+        
+        partPercentages = list()
+        avgDisconnectedCols = list()
+        for mal in mals:
+            isPartedCount = partCount = 0
+            validatorCountPerColumn = getValidatorCountPerColumn(numberOfColumns, nv, custody)
+            for _run in range(runs):
+                _isPartedCount, _partCount = runOnce(_run, runs, deg, validatorCountPerColumn, mal)
+                isPartedCount += _isPartedCount
+                partCount += _partCount
+            
+            partPercentages.append(isPartedCount * 100 / runs)
+            avgDisconnectedCols.append(partCount / runs)
+            print(f"Malicious Nodes: {mal}%, Partition Percentage: {partPercentages[-1]}, Avg. Partitions: {avgDisconnectedCols[-1]}")
+        
+        nnPartPercentages[nn] = partPercentages
+        nnAvgDisconnectedCols[nn] = avgDisconnectedCols
+    
+    now = datetime.now()
+    execID = now.strftime("%Y-%m-%d_%H-%M-%S_")+str(random.randint(100,999))
+    newpath = f"ConnectivityTest/MaliciousNodesVsNumberOfNodes/results/{execID}/"
+    if not os.path.exists(newpath): os.makedirs(newpath)
+    
+    conf1 = {
+        'x': mals,
+        'y': nnPartPercentages,
+        'label': "Nodes",
+        'xlabel': "Malicious Node (%)",
+        'ylabel': "Partition Possibility (%)",
+        "title": "Possibility of Network Graph Get Partitioned for Malicious Nodes",
+        "plotPath": f"{newpath}prob.png"
+    }
+    
+    conf2 = {
+        'x': mals,
+        'y': nnAvgDisconnectedCols,
+        'label': "Nodes",
+        'xlabel': "Malicious Node (%)",
+        'ylabel': "Avg. Disconnected Columns",
+        "title": "Malicious Nodes (%) vs. Disconnected Columns",
+        "plotPath": f"{newpath}num.png"
+    }
+    
+    plotData(conf1)
+    plotData(conf2)
+
+
+# Configuration
+runs = 10
+deg = 8
+mals = range(5, 100, 5)
+numberOfColumns = 128
+custody = 4
+numberOfNodes = [int(_) for _ in (np.logspace(2, 3, 3, endpoint=True, base=10) * 5)]
+numberOfValidators = [int(nn * 2.4) for nn in numberOfNodes] # (0.8 * 1 + 0.2 * 8 = 2.4)
+
+if __name__ == "__main__":
+    study()