I am estimating a nested_block_model with bipartite data and am getting a
strange result. There are about 4,000 nodes in the network and I am
estimating a model by:
state_dc = minimize_nested_blockmodel_dl(G, state_args=dict(deg_corr=True,
for i in range(1000): # this should be sufficiently large
The results find 13 nested levels, but after level 5 there are only 2
nonempty blocks found in each additional level. I'm not sure if this is
really a problem, or I can just ignore the levels where nothing really
changes. Any advice/help would be greatly appreciated. Thank you
Assistant Professor, Political Science
Hi. I'm interested in finding subgraph isomorphisms on graphs representing
tensor networks. Nodes of such graphs contain expressions (but here one
could think of arithmetic expressions over +-/* and variables). Let's say I
want to find an isomorphism between my graph and a pattern graph, which
contains more generic expressions. Comparing expressions directly doesn't
make much sense because of variables, so some form of unification should
be used instead.
What strategy could I use to fit the existing API of subgraph_isomorphism
which currently only allows me to calculate some fixed labels for nodes?
Is it hard(feasible) to modify the API by adding some form of predicate
comparison of nodes?