I saw that one as well. Looks neat! Too bad they rarely mention the word "graph" so they never come up on my google searches. I found them through del.icio.us by searching for python and graph. Dave On 8/1/06, Pau Gargallo <pau.gargallo@gmail.com> wrote:
you may be interested in this python graph library https://networkx.lanl.gov/
pau
I actually just looked into the boost graph library and hit a wall. I basically had trouble running bjam on it. It complained about a missing build file or something like that.
Anyways, for now I can live with non-sparse implementation. This is mostly prototyping code for integeration in to a largely Java system (with some things written in C). So this will be ported to Java or C eventually. Whether or not I will need to protoype something that scales to
nodes remains to be seen.
Dave
On 8/1/06, Charles R Harris <charlesr.harris@gmail.com> wrote:
Hi David,
I often have several thousand nodes in a graph, sometimes clustered
into connected components. I suspect that using an adjacency matrix is an inefficient representation for graphs of that size while for smaller graphs the overhead of more complicated structures wouldn't be noticeable. Have you looked at the boost graph library? I don't like all their stuff but it is a good start with lots of code and a suitable license.
Chuck
On 8/1/06, David Grant < davidgrant@gmail.com> wrote:
I have written my own graph class, it doesn't really do much, just has
a few methods, it might do more later. Up until now it has just had one
of data, an adjacency matrix, so it looks something like this:
class Graph: def __init__(self, Adj): self.Adj = Adj
I had the idea of changing Graph to inherit numpy.ndarray instead, so
I can just access itself directly rather than having to type self.Adj. Is this the right way to go about it? To inherit from numpy.ndarray?
The reason I'm using a numpy array to store the graph by the way is
On 8/1/06, David Grant <davidgrant@gmail.com> wrote: thousands of piece then the
-Memory is not a concern (yet) so I don't need to use a sparse structure
following: like a sparse array or a dictionary
-I run a lot of sums on it, argmin, blanking out of certain rows and
columns using fancy indexing, grabbing subgraphs using vector indexing > > > -- > David Grant > http://www.davidgrant.ca > >
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