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Description
First off, thanks for developing and sharing this interesting package. I've forked the repo and all test cases work fine. This is probably related to #14, but I've made a new issue because I have data.
I have an adjacency matrix from a large directed graph. The dimensions of the adjacency matrix are (7919711, 7116242)
and the structure is extremely sparse, number of non-zero elements are 2732656
.
When I try to run the pooling on a subset (10000x10000)
of my own data that you can find here (5.07 KB file) I can produce errors with the flavour (ran on sparse_adj_subset)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-91-7776269d0a82> in <module>()
----> 1 graphs, perm = coarsening.coarsen(ajd_sparse_ss, levels=3, self_connections=False)
~/projects/erst/graph-embedding/lib/coarsening.py in coarsen(A, levels, self_connections)
8 levels.
9 """
---> 10 graphs, parents = metis(A, levels)
11 perms = compute_perm(parents)
12
~/projects/erst/graph-embedding/lib/coarsening.py in metis(W, levels, rid)
80 cc = idx_col[perm]
81 vv = val[perm]
---> 82 cluster_id = metis_one_level(rr,cc,vv,rid,weights) # rr is ordered
83 parents.append(cluster_id)
84
~/projects/erst/graph-embedding/lib/coarsening.py in metis_one_level(rr, cc, vv, rid, weights)
140 for ii in range(N):
141 tid = rid[ii]
--> 142 if not marked[tid]:
143 wmax = 0.0
144 rs = rowstart[tid]
IndexError: index 9977 is out of bounds for axis 0 with size 9974
And if I rerun, I get
index 9994 is out of bounds for axis 0 with size 9974
showing that the index changes.
I am running with graphs, perm = coarsening.coarsen(ajd_sparse_ss, levels=3, self_connections=False)
but setting self_connections=True
gives similar problems.