def preferential_attachment_prediction(trainGraph, testGraph, mode, layer2,
type):
adjacencyMatrix = testGraph.get_adjacency()
if mode == "multilayer":
PAMatrix = multilayer_preferential_attachment_score(
trainGraph, layer2, type)
if mode == "normal":
PAMatrix = preferential_attachment_score(trainGraph)
PAMatrix = np.array(PAMatrix)
i = (-PAMatrix).argsort(axis=None, kind='mergesort')
j = np.unravel_index(i, PAMatrix.shape)
sortedIndices = np.vstack(j).T
total = len(PAMatrix) * len(PAMatrix)
sortedList = np.sort(np.array(PAMatrix).ravel())
roc_auc = calculate_auroc(sortedList[::-1], sortedIndices, adjacencyMatrix)
#roc_auc = auc(fpr, tpr)
#print ("Area under the ROC curve : %f" % roc_auc)
#return roc_auc,fpr,tpr
#return roc_auc,precision,recall
return roc_auc, sortedIndices
def common_neighbors_prediction(trainGraph,testGraph,mode,layer2,type):
adjacencyMatrix = testGraph.get_adjacency()
if mode == "multilayer":
CNMatrix = multilayer_common_neighbors_score(trainGraph,layer2,type)
if mode == "normal":
CNMatrix = common_neighbors_score(trainGraph)
CNMatrix = np.array(CNMatrix)
i = (-CNMatrix ).argsort(axis=None, kind='mergesort')
j = np.unravel_index(i, CNMatrix .shape)
sortedIndices = np.vstack(j).T
sortedList = np.sort(np.array(CNMatrix).ravel())
roc_auc = calculate_auroc(sortedList[::-1],sortedIndices,adjacencyMatrix)
#roc_auc = auc(fpr, tpr)
#print ("Area under the ROC curve : %f" % roc_auc)
#return roc_auc,fpr,tpr
#return roc_auc,precision,recall
return roc_auc,sortedIndices
pairs = []
scores = []
N = tradesTrainGraph.vcount()
for v in range(N):
for w in range(N):
pairs.append((v, w))
if v in tradeNodeRates:
scores.append(
tradeNodeRates.get(v) + (tradeLayerWeights[0] * 1))
else:
scores.append(0)
sortedIndices, sortedList = memScoreMatch(pairs, scores)
adjacencyMatrix = tradesTrainGraph.get_adjacency()
auroc = calculate_auroc(sortedList, sortedIndices, adjacencyMatrix)
print(auroc)
scores = []
pairs = []
N = messagesTrainGraph.vcount()
for v in range(N):
for w in range(N):
pairs.append((v, w))
if v in messageNodeRates:
scores.append(
messageNodeRates.get(v) + (messageLayerWeights[0] * 1))
else:
scores.append(0)
sortedIndices, sortedList = memScoreMatch(pairs, scores)