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  1. PO
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    <p>You can load your data with something like this:</p> <pre><code>import numpy as np import scipy.sparse as sps data = np.genfromtxt('data.txt', dtype=[('from', np.intp), ('to', np.intp), ('weight', np.float)]) </code></pre> <p>If you want to store your weights in a sparse matrix <code>graph</code>, where <code>graph[i, j]</code> is the weight from node <code>i</code> to node <code>j</code>, you could do:</p> <pre><code>graph = sps.csr_matrix((data['weight'], (data['from'], data['to']))) </code></pre> <p>To have a sorted list of outgoing nodes, I would use a dictionary, where <code>sorted_to</code> is an array of outgoing nodes sorted by weight. It's a little hacky, and dependent on the CSR sparse matrix format, but you could do:</p> <pre><code>graph = sps.rand(10, 10, density=0.1, format='csr') data, indptr, indices = graph.data, graph.indptr, graph.indices non_empty_rows, = np.nonzero(np.diff(graph.indptr)) sorted_out = {} for j in non_empty_rows: weight_slice = data[indptr[j]:indptr[j+1]] out_slice = indices[indptr[j]:indptr[j+1]] sorted_out[j] = out_slice[np.argsort(weight_slice)] </code></pre> <p>With a simple made up example:</p> <pre><code>&gt;&gt;&gt; graph = sps.rand(5, 5, density=0.2, format='csr') &gt;&gt;&gt; graph.toarray() array([[ 0.88968871, 0. , 0. , 0.80773932, 0. ], [ 0. , 0. , 0.8921645 , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. ], [ 0.18552664, 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0.22945956]]) &gt;&gt;&gt; non_empty_rows array([0, 1, 3, 4], dtype=int64) &gt;&gt;&gt; sorted_out {0: array([3, 0]), 1: array([2]), 3: array([0]), 4: array([4])} </code></pre>
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    1. POHow to read a sparse directed graph in scipy
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    1. POHow to read a sparse directed graph in scipy
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    1. COThank you. It looks like this effectively stores the graph twice however. Once in graph and once in sorted_out. Is that the idea?
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    2. COYou want to know your weights, and their sorting order without sorting, so you are going to need 2 values per vertex: the weight, and the order in the sorted list. So yes. You could have a single dictionary that stored the same amount of data, but you would then miss all the cool things you can do with a graph in matrix format, but it all depends in what you are after.
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    3. COOK that makes sense but is a shame.
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