Am 11.07.21 um 23:31 schrieb [email protected]:
Hi,
I'm sorry, I'll try to be more specific.
I eventually need a matrix that I can plot as done in this post by ulgenklc:
https://nabble.skewed.de/Temporal-community-detection-with-DSBM-tt4028581.html#a4028592
Here, I use a subgraph with only two layers to demonstrate this seemingly
trivial issue:
np.random.seed(42)
gt.seed_rng(42)
state = minimize_nested_blockmodel_dl(g, state_args=dict(base_type=LayeredBlockState,
state_args=dict(deg_corr=True, overlap=True, layers=True, ec=g.ep.layer,
recs=[g.ep.weight], rec_types=["discrete-binomial"])))
This yields:
<NestedBlockState object, with base <LayeredBlockState object with 4610 overlapping
blocks, 2 layers, degree-corrected, with 2 edge covariates, for graph <Graph object,
undirected, with 82 vertices and 2305 edges, 2 internal vertex properties, 4 internal edge
properties, and 8 levels of sizes [(82, 3), (3, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1),
(1, 1)] at 0x2b1aa8982dc0>
While there are 4610 overlapping blocks in this stage, state.draw() divides the
nodes neatly into three sensible communities. Hence, I know that the snippet of
code that I need already exists somewhere in draw_hierarchy.
Then, I filter the edges in state.g by layer and use get_edge_blocks() to
extract the layer-specific half-edges:
lay = []
for e in g.edges():
lay.append(g.ep.layer[e]==0) #edges in the first layer
lay = np.array(lay, dtype="int")
ft = g.new_edge_property("bool")
ft.a = lay
state.g.set_edge_filter(ft)
be = state.levels[0].get_edge_blocks()
This is where I struggle to find a way to extract the desired information. My
best guess on how to extract the layer-specific node memberships from _be_ is
the following:
s = OverlapBlockState(state.g, b=be) #state.g with filter on
mb = s.get_majority_blocks()
for v in g.vertices():
print(mb[v])
If I compare the _mb_ for the discrete layers, the memberships vary slightly
(this is what I need), but I'm unsure whether this is the correct way to
extract this information?
If "be" is the property map returned by
state.levels[0].get_edge_blocks(), then
bm = g.new_vp("vector<int>", val=[0] * state.levels[0].get_B())
for e in g.edges():
if layer[e] != 0:
continue
u, v = e
u, v = sorted((u, v))
r, s = be[e]
bm[u][r] += 1
bm[v][s] += 1
will give you the memberships in each group at layer 0 in the property
map "bm". Is this what you want?
Best,
Tiago
--
Tiago de Paula Peixoto <[email protected]>
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