1 | initial version |
Assuming the input consists of n
, r
, and the probabilities for edges within a community; and assuming that the probability of edges between vertices in different communities is 0, then here is a possible way to generate such a graph.
community_sizes = Partitions(n,length=r).random_element()
H = Graph()
communities = []
for comm_size,Pr in zip(community_sizes,probs):
H=H.disjoint_union(graphs.RandomGNP(comm_size,Pr))
This is assuming that n
, r
and probs
are given as part of the input. To see the resulting graph you can use H.show()
.
2 | No.2 Revision |
Assuming the input consists of n
, r
, and the probabilities for edges within a community; and assuming that the probability of edges between vertices in different communities is 0, then here is a possible way to generate such a graph.
community_sizes = Partitions(n,length=r).random_element()
H = Graph()
communities = []
for comm_size,Pr in zip(community_sizes,probs):
H=H.disjoint_union(graphs.RandomGNP(comm_size,Pr))
H=H.disjoint_union(graphs.RandomGNP(comm_size,Pr),labels='integers')
This is assuming that n
, r
and probs
are given as part of the input. To see the resulting graph you can use H.show()
.
3 | No.3 Revision |
Assuming the input consists of n
, r
, and the probabilities for edges within a community; and assuming that the probability of edges between vertices in different communities is 0, then here is a possible way to generate such a graph.
community_sizes = Partitions(n,length=r).random_element()
H = Graph()
communities = []
for comm_size,Pr in zip(community_sizes,probs):
H=H.disjoint_union(graphs.RandomGNP(comm_size,Pr),labels='integers')
This is assuming that n
, r
and probs
are given as part of the input. To see the resulting graph you can use H.show()
.
4 | No.4 Revision |
Assuming the input consists of n
, r
, and the probabilities for edges within a community; and assuming that the probability of edges between vertices in different communities is 0, then here is a possible way to generate such a graph.
community_sizes = Partitions(n,length=r).random_element()
H = Graph()
for comm_size,Pr in zip(community_sizes,probs):
H=H.disjoint_union(graphs.RandomGNP(comm_size,Pr),labels='integers')
This is assuming that n
, r
and probs
(assumed to be a list of length r
containing $P_1,P_2,\ldots,P_r$) are given as part of the input. To see the resulting graph you can use H.show()
.