Also with the correct code for "similarity inverse log weighted"
a = i.similarity_inverse_log_weighted(vertices=33,23)
This is definitely not correct Python syntax; you have a positional argument
(23) after a keyword argument (vertices=...) - although this has nothing to
do with the MemoryError of course.
I have just generated a random graph with 20000 vertices and it seems to
work with similarity_inverse_log_weighted, so I don't know what's going on here:
g = Graph.GRG(20000, 0.01)
print g.vcount()
20000
print g.ecount()
62685
a = g.similarity_inverse_log_weighted(vertices=33)
len(a)
1
len(a[0])
20000
So it seems to work. If you still have problems, please send me your graph
so I can take a look at it.
Does this function also convert the graph into a matrix like object?
No, it does not. The result will be a list of lists, however, that's why you
shouldn't simply pass all the vertices to the function (because the
resulting matrix would be too large to keep it in memory for a graph with
20K vertices), but it should work if you pass vertices in small batches.
--
T.
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