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"""

FP-GraphMiner - A Fast Frequent Pattern Mining Algorithm for Network Graphs

A novel Frequent Pattern Graph Mining algorithm, FP-GraphMiner, that compactly

represents a set of network graphs as a Frequent Pattern Graph (or FP-Graph).

This graph can be used to efficiently mine frequent subgraphs including maximal

frequent subgraphs and maximum common subgraphs.

URL: https://www.researchgate.net/publication/235255851

"""

# fmt: off

edge_array = [

['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12', 'cd-e2', 'ce-e4',

'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3', 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3'],

['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'cd-e2', 'de-e1', 'df-e8',

'ef-e3', 'eg-e2', 'fg-e6'],

['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'de-e1', 'df-e8', 'dg-e5', 'ef-e3', 'eg-e2',

'eh-e12', 'fg-e6', 'fh-e10', 'gh-e6'],

['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'bh-e12', 'cd-e2', 'df-e8', 'dh-e10'],

['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'cd-e2', 'ce-e4', 'de-e1', 'df-e8',

'dg-e5', 'ef-e3', 'eg-e2', 'fg-e6']

]

# fmt: on

def get_distinct_edge(edge_array):

"""

Return Distinct edges from edge array of multiple graphs

>>> sorted(get_distinct_edge(edge_array))

['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']

"""

distinct_edge = set()

for row in edge_array:

for item in row:

distinct_edge.add(item[0])

return list(distinct_edge)

def get_bitcode(edge_array, distinct_edge):

"""

Return bitcode of distinct_edge

"""

bitcode = ["0"] * len(edge_array)

for i, row in enumerate(edge_array):

for item in row:

if distinct_edge in item[0]:

bitcode[i] = "1"

break

return "".join(bitcode)

def get_frequency_table(edge_array):

"""

Returns Frequency Table

"""

distinct_edge = get_distinct_edge(edge_array)

frequency_table = dict()

for item in distinct_edge:

bit = get_bitcode(edge_array, item)

# print('bit',bit)

# bt=''.join(bit)

s = bit.count("1")

frequency_table[item] = [s, bit]

# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order

sorted_frequency_table = [

[k, v[0], v[1]]

for k, v in sorted(frequency_table.items(), key=lambda v: v[1][0], reverse=True)

]

return sorted_frequency_table

def get_nodes(frequency_table):

"""

Returns nodes

format nodes={bitcode:edges that represent the bitcode}

>>> get_nodes([['ab', 5, '11111'], ['ac', 5, '11111'], ['df', 5, '11111'],

... ['bd', 5, '11111'], ['bc', 5, '11111']])

{'11111': ['ab', 'ac', 'df', 'bd', 'bc']}

"""

nodes = {}

for i, item in enumerate(frequency_table):

nodes.setdefault(item[2], []).append(item[0])

return nodes

def get_cluster(nodes):

"""

Returns cluster

format cluster:{WT(bitcode):nodes with same WT}

"""

cluster = {}

for key, value in nodes.items():

cluster.setdefault(key.count("1"), {})[key] = value

return cluster

def get_support(cluster):

"""

Returns support

>>> get_support({5: {'11111': ['ab', 'ac', 'df', 'bd', 'bc']},

... 4: {'11101': ['ef', 'eg', 'de', 'fg'], '11011': ['cd']},

... 3: {'11001': ['ad'], '10101': ['dg']},

... 2: {'10010': ['dh', 'bh'], '11000': ['be'], '10100': ['gh'],

... '10001': ['ce']},

... 1: {'00100': ['fh', 'eh'], '10000': ['hi']}})

[100.0, 80.0, 60.0, 40.0, 20.0]

"""

return [i * 100 / len(cluster) for i in cluster]

def print_all() -> None:

print("\nNodes\n")

for key, value in nodes.items():

print(key, value)

print("\nSupport\n")

print(support)

print("\n Cluster \n")

for key, value in sorted(cluster.items(), reverse=True):

print(key, value)

print("\n Graph\n")

for key, value in graph.items():

print(key, value)

print("\n Edge List of Frequent subgraphs \n")

for edge_list in freq_subgraph_edge_list:

print(edge_list)

def create_edge(nodes, graph, cluster, c1):

"""

create edge between the nodes

"""

for i in cluster[c1].keys():

count = 0

c2 = c1 + 1

while c2 < max(cluster.keys()):

for j in cluster[c2].keys():

"""

creates edge only if the condition satisfies

"""

if int(i, 2) & int(j, 2) == int(i, 2):

if tuple(nodes[i]) in graph:

graph[tuple(nodes[i])].append(nodes[j])

else:

graph[tuple(nodes[i])] = [nodes[j]]

count += 1

if count == 0:

c2 = c2 + 1

else:

break

def construct_graph(cluster, nodes):

X = cluster[max(cluster.keys())]

cluster[max(cluster.keys()) + 1] = "Header"

graph = {}

for i in X:

if tuple(["Header"]) in graph:

graph[tuple(["Header"])].append(X[i])

else:

graph[tuple(["Header"])] = [X[i]]

for i in X:

graph[tuple(X[i])] = [["Header"]]

i = 1

while i < max(cluster) - 1:

create_edge(nodes, graph, cluster, i)

i = i + 1

return graph

def myDFS(graph, start, end, path=None):

"""

find different DFS walk from given node to Header node

"""

path = (path or []) + [start]

if start == end:

paths.append(path)

for node in graph[start]:

if tuple(node) not in path:

myDFS(graph, tuple(node), end, path)

def find_freq_subgraph_given_support(s, cluster, graph):

"""

find edges of multiple frequent subgraphs

"""

k = int(s / 100 * (len(cluster) - 1))

for i in cluster[k].keys():

myDFS(graph, tuple(cluster[k][i]), tuple(["Header"]))

def freq_subgraphs_edge_list(paths):

"""

returns Edge list for frequent subgraphs

"""

freq_sub_EL = []

for edges in paths:

EL = []

for j in range(len(edges) - 1):

temp = list(edges[j])

for e in temp:

edge = (e[0], e[1])

EL.append(edge)

freq_sub_EL.append(EL)

return freq_sub_EL

def preprocess(edge_array):

"""

Preprocess the edge array

>>> preprocess([['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12',

... 'cd-e2', 'ce-e4', 'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3',

... 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3']])

"""

for i in range(len(edge_array)):

for j in range(len(edge_array[i])):

t = edge_array[i][j].split("-")

edge_array[i][j] = t

if __name__ == "__main__":

preprocess(edge_array)

frequency_table = get_frequency_table(edge_array)

nodes = get_nodes(frequency_table)

cluster = get_cluster(nodes)

support = get_support(cluster)

graph = construct_graph(cluster, nodes)

find_freq_subgraph_given_support(60, cluster, graph)

paths: list = []

freq_subgraph_edge_list = freq_subgraphs_edge_list(paths)

print_all()

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