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

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