I currently started using graph-tool: nice software, excellent documentation, thank you!
However, I stumbled on some (for me) unexpected behavior when using the get_edges_prob() function with a BlockState of a weighted network (graph-tool version 2.26, Python 2.7 as well as Python 3.6). When calling the get_edges_prob() function to a state, its entropy is altered, and subsequent calls of get_edges_prob() deliver different results.
"Luckily", I could reproduce the observed behavior with a dataset from the graph-tool collection (with arguably small alterations, but the introduced differences are a lot bigger in my networks).
import graph_tool as gt
import graph_tool.collection as gtc
import graph_tool.inference as gti
g=gtc.data["celegansneural"]
state=gti.minimize_blockmodel_dl(g,state_args=dict(recs=[g.ep.value],rec_types=['real-normal']))
original_entropy=state.entropy()
edge_prob=[]
for i in range(10000):
edge_prob.append(state.get_edges_prob(missing=[],spurious=[(0,2)]))
original_entropy
state.entropy() #entropy is different from original!
edge_prob[0] #first call of get_edges_prob() delivers other results than last
edge_prob[-1]
For me, this is really unexpected. What is happening there, and/or how this can be fixed?
Smaller further experiments showed that this also happens to NestedBlockStates (of course), but seems not to happen for models lacking edge covariates...
Thanks in advance!
Katharina
Katharina Baum, PhD
Postdoctoral Fellow, Proteome and Genome Research Unit
Luxembourg Institute of Health
Department of Oncology
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