def __init__(self, G):
self._Gr = G
self._coupleDistances = {}
for v in self._Gr.getVertices():
v_sssp = SSSP(self._Gr)
v_sssp.run(v)
self._coupleDistances[v] = v_sssp.getDistances()
def getCommunities(self, k):
"""
Returns a set of communities detected by removal of k edges with
maximal betweenness centrality.
@Invariant: all([all([self._G.isVertex(v) for v in C]) for C in self.getCommunities()])
"""
unDirectG = self.directToUndirectGraph(self._G)
unDirectG = deepcopy(unDirectG)
community_vertices = set()
if k <=0:
return [unDirectG.getVertices()]
if k > len(unDirectG.getEdges()):
ans = []
for ver in unDirectG.getVertices():
ans.append([ver])
return ans
for i in range(0,k):
bweenS = EdgeBetweenness(unDirectG)
bweenS.run()
biggestEdgeBC = bweenS.getTheBiggestBC_Edges()
if biggestEdgeBC is None:
break
unDirectG.deleteEdge(biggestEdgeBC)
unDirectG.deleteEdge((biggestEdgeBC[1],biggestEdgeBC[0]))
community_vertices.add(biggestEdgeBC[0])
community_vertices.add(biggestEdgeBC[1])
ans = []
for v in community_vertices:
v_community = []
bfs_from_v = SSSP(unDirectG)
bfs_from_v.run(v)
for reachable_from_v in bfs_from_v._distances:
if bfs_from_v._distances[reachable_from_v] >= 0:
v_community.append(reachable_from_v)
isExist = False
for elm in ans:
if elm == v_community:
isExist = True
break
if isExist == False:
ans.append(v_community)
return ans
g.addEdge((1, 4, 7))
g.addEdge((4, 3, 6))
g.addEdge((3, 2, -5))
g.addEdge((1, 3, 1))
g.addEdge((3, 0, 2))
g.print()
distance = fastDPAlgo(g)
print("\nShortest path distances by BASIC DP ALGORITHM:")
printMatrix(distance, g.v)
distance = floydWarshall(g)
print("\nShortest path distances by FLOYD-WARSHALL ALGORITHM:")
printMatrix(distance, g.v)
sparseGraph = SSSP()
for i in range(5):
sparseGraph.addVertex(i)
sparseGraph.addDirectedEdge((0, 4, -4))
sparseGraph.addDirectedEdge((0, 1, 3))
sparseGraph.addDirectedEdge((2, 1, 4))
sparseGraph.addDirectedEdge((0, 2, 8))
sparseGraph.addDirectedEdge((1, 4, 7))
sparseGraph.addDirectedEdge((4, 3, 6))
sparseGraph.addDirectedEdge((3, 2, -5))
sparseGraph.addDirectedEdge((1, 3, 1))
sparseGraph.addDirectedEdge((3, 0, 2))
distance = johnson(sparseGraph)
print("\nShortest path distances by JOHNSON ALGORITHM:")
printMatrix(distance, len(distance))
