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```python
def bs(g,s,c):
#print "s is {}".format(s)
#print "c is {}".format(c)
if len(c) == 0:
return s
v = c[0]
#print "v is {}".format(v)
SCopy = copy(s…
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LightGraphs has a function to compute the modularity of a graph.
However, it has been shown in several papers that a different definition of modularity for bipartite graphs can be used for more accur…
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# colour:
Currently, the adjacency graphs assume the nodes of the graph are all the same 'colour' or type. This has implications for the size and memory layout of the graphs. However, if one allow…
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See http://dx.doi.org/10.1016/j.physa.2006.04.047
And the code is actually here:
http://jlguillaume.free.fr/www/programs.php?lang=eng
---
Imported from Launchpad using lp2gh.
- date created: 2010-1…
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HPR adds huge training cost to baselines:
1. all three branches need bipartite matching to calculate losses
2. Data Re-Augmentation doubles the training images (possibly equals to more training sche…
fushh updated
2 months ago
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### Description:
We should implement model sharding in neural-lam to allow for training with larger batch sizes without exhausting GPU vRAM. This feature will enable users to scale to larger models a…
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Hi @epatters, following up here from the zulip conversation. Before working on `PropertyBipartiteGraph` and `to_graphviz` for it, I wanted to ask if you can help explain the design considerations behi…
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A bipartite network is a type of network or graph that consists of two sets of vertices, where vertices from one set are only connected to vertices from the other set, and vice versa. In our case, the…
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@dpbisme suggested possibly filtering nodes by degree for ex, or centrality
i assume this is by user control