mirror of
https://github.com/TheAlgorithms/Python.git
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233 lines
7.2 KiB
Python
233 lines
7.2 KiB
Python
"""
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FP-GraphMiner - A Fast Frequent Pattern Mining Algorithm for Network Graphs
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A novel Frequent Pattern Graph Mining algorithm, FP-GraphMiner, that compactly
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represents a set of network graphs as a Frequent Pattern Graph (or FP-Graph).
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This graph can be used to efficiently mine frequent subgraphs including maximal
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frequent subgraphs and maximum common subgraphs.
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URL: https://www.researchgate.net/publication/235255851
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"""
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# fmt: off
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edge_array = [
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['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12', 'cd-e2', 'ce-e4',
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'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3', 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3'],
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['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'cd-e2', 'de-e1', 'df-e8',
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'ef-e3', 'eg-e2', 'fg-e6'],
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['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'de-e1', 'df-e8', 'dg-e5', 'ef-e3', 'eg-e2',
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'eh-e12', 'fg-e6', 'fh-e10', 'gh-e6'],
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['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'bh-e12', 'cd-e2', 'df-e8', 'dh-e10'],
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['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'cd-e2', 'ce-e4', 'de-e1', 'df-e8',
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'dg-e5', 'ef-e3', 'eg-e2', 'fg-e6']
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]
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# fmt: on
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def get_distinct_edge(edge_array):
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"""
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Return Distinct edges from edge array of multiple graphs
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>>> sorted(get_distinct_edge(edge_array))
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['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
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"""
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distinct_edge = set()
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for row in edge_array:
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for item in row:
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distinct_edge.add(item[0])
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return list(distinct_edge)
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def get_bitcode(edge_array, distinct_edge):
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"""
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Return bitcode of distinct_edge
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"""
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bitcode = ["0"] * len(edge_array)
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for i, row in enumerate(edge_array):
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for item in row:
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if distinct_edge in item[0]:
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bitcode[i] = "1"
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break
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return "".join(bitcode)
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def get_frequency_table(edge_array):
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"""
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Returns Frequency Table
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"""
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distinct_edge = get_distinct_edge(edge_array)
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frequency_table = {}
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for item in distinct_edge:
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bit = get_bitcode(edge_array, item)
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# print('bit',bit)
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# bt=''.join(bit)
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s = bit.count("1")
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frequency_table[item] = [s, bit]
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# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
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sorted_frequency_table = [
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[k, v[0], v[1]]
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for k, v in sorted(frequency_table.items(), key=lambda v: v[1][0], reverse=True)
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]
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return sorted_frequency_table
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def get_nodes(frequency_table):
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"""
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Returns nodes
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format nodes={bitcode:edges that represent the bitcode}
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>>> get_nodes([['ab', 5, '11111'], ['ac', 5, '11111'], ['df', 5, '11111'],
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... ['bd', 5, '11111'], ['bc', 5, '11111']])
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{'11111': ['ab', 'ac', 'df', 'bd', 'bc']}
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"""
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nodes = {}
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for _, item in enumerate(frequency_table):
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nodes.setdefault(item[2], []).append(item[0])
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return nodes
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def get_cluster(nodes):
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"""
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Returns cluster
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format cluster:{WT(bitcode):nodes with same WT}
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"""
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cluster = {}
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for key, value in nodes.items():
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cluster.setdefault(key.count("1"), {})[key] = value
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return cluster
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def get_support(cluster):
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"""
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Returns support
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>>> get_support({5: {'11111': ['ab', 'ac', 'df', 'bd', 'bc']},
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... 4: {'11101': ['ef', 'eg', 'de', 'fg'], '11011': ['cd']},
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... 3: {'11001': ['ad'], '10101': ['dg']},
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... 2: {'10010': ['dh', 'bh'], '11000': ['be'], '10100': ['gh'],
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... '10001': ['ce']},
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... 1: {'00100': ['fh', 'eh'], '10000': ['hi']}})
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[100.0, 80.0, 60.0, 40.0, 20.0]
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"""
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return [i * 100 / len(cluster) for i in cluster]
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def print_all() -> None:
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print("\nNodes\n")
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for key, value in nodes.items():
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print(key, value)
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print("\nSupport\n")
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print(support)
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print("\n Cluster \n")
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for key, value in sorted(cluster.items(), reverse=True):
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print(key, value)
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print("\n Graph\n")
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for key, value in graph.items():
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print(key, value)
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print("\n Edge List of Frequent subgraphs \n")
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for edge_list in freq_subgraph_edge_list:
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print(edge_list)
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def create_edge(nodes, graph, cluster, c1):
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"""
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create edge between the nodes
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"""
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for i in cluster[c1]:
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count = 0
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c2 = c1 + 1
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while c2 < max(cluster.keys()):
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for j in cluster[c2]:
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"""
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creates edge only if the condition satisfies
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"""
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if int(i, 2) & int(j, 2) == int(i, 2):
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if tuple(nodes[i]) in graph:
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graph[tuple(nodes[i])].append(nodes[j])
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else:
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graph[tuple(nodes[i])] = [nodes[j]]
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count += 1
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if count == 0:
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c2 = c2 + 1
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else:
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break
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def construct_graph(cluster, nodes):
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x = cluster[max(cluster.keys())]
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cluster[max(cluster.keys()) + 1] = "Header"
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graph = {}
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for i in x:
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if (["Header"],) in graph:
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graph[(["Header"],)].append(x[i])
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else:
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graph[(["Header"],)] = [x[i]]
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for i in x:
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graph[(x[i],)] = [["Header"]]
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i = 1
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while i < max(cluster) - 1:
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create_edge(nodes, graph, cluster, i)
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i = i + 1
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return graph
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def my_dfs(graph, start, end, path=None):
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"""
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find different DFS walk from given node to Header node
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"""
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path = (path or []) + [start]
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if start == end:
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paths.append(path)
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for node in graph[start]:
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if tuple(node) not in path:
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my_dfs(graph, tuple(node), end, path)
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def find_freq_subgraph_given_support(s, cluster, graph):
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"""
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find edges of multiple frequent subgraphs
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"""
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k = int(s / 100 * (len(cluster) - 1))
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for i in cluster[k]:
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my_dfs(graph, tuple(cluster[k][i]), (["Header"],))
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def freq_subgraphs_edge_list(paths):
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"""
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returns Edge list for frequent subgraphs
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"""
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freq_sub_el = []
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for edges in paths:
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el = []
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for j in range(len(edges) - 1):
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temp = list(edges[j])
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for e in temp:
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edge = (e[0], e[1])
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el.append(edge)
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freq_sub_el.append(el)
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return freq_sub_el
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def preprocess(edge_array):
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"""
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Preprocess the edge array
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>>> preprocess([['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12',
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... 'cd-e2', 'ce-e4', 'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3',
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... 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3']])
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"""
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for i in range(len(edge_array)):
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for j in range(len(edge_array[i])):
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t = edge_array[i][j].split("-")
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edge_array[i][j] = t
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if __name__ == "__main__":
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preprocess(edge_array)
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frequency_table = get_frequency_table(edge_array)
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nodes = get_nodes(frequency_table)
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cluster = get_cluster(nodes)
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support = get_support(cluster)
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graph = construct_graph(cluster, nodes)
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find_freq_subgraph_given_support(60, cluster, graph)
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paths: list = []
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freq_subgraph_edge_list = freq_subgraphs_edge_list(paths)
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print_all()
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