Dear Graph-Tool community,
I am trying to construct a graph with a large number of edges, using an np file
as an edge_list.
There are 125760 vertices and an edge list of length (7907725920, 2).
In order to use the npy edge_list file, I have needed to load the edge list as
a readable memmap because, at a size of 126Gb, it is far too large to load into
memory. But, when calling add_edge_list to this memmap, I think it is still
being loaded into memory as the RAM will fill and the python session crash. I
suppose the alternative is that the graph object becomes too large to hold into
memory, but with previous large graphs I did not find this to be the problem.
Does anybody have a solution to this issue?
Lastly, after I find a means to add this number of edges, I need to assign
weights to the edges, again from a memmap file due to its size, which gives me
the same problem. Any advice?
Sample code:
#prime the graph with the number of vertices
g = Graph(directed=False)
g.add_vertex(125760)
#load the edge list as memmap and add it
idx_indi_mmap = np.load('idx_indi.npy', mmap_mode='r’)
idx_indi_mmap.shape #(7907725920, 2)
g.add_edge_list(idx_indi_mmap) #script will crash at this point from filling
the RAM
#Then want to add weights by taking the indices from another memmap object
node_matrix = np.load('node_matrix.npy', mmap_mode='r’)
node_matrix.shape #(125760, 125760)
weights = node_matrix[idx_indi_mmap]
ew = g("double")
ew.a = weights
g.ep[‘weight'] = ew
Thank you for your help!
James
_______________________________________________
graph-tool mailing list
graph-tool@skewed.de
https://lists.skewed.de/mailman/listinfo/graph-tool
