def _edge_betweenness(G, source, nodes, cutoff=False):
"""
Edge betweenness helper.
"""
between = {}
# get the predecessor data
#(pred,length)=_fast_predecessor(G,source,cutoff=cutoff)
(pred, length) = predecessor(G, source, cutoff=cutoff, return_seen=True)
# order the nodes by path length
onodes = [
nn for dd, nn in sorted((dist, n) for n, dist in length.iteritems())
]
# intialize betweenness, doesn't account for any edge weights
for u, v in G.edges(nodes):
between[(u, v)] = 1.0
between[(v, u)] = 1.0
while onodes: # work through all paths
v = onodes.pop()
if v in pred:
num_paths = len(pred[v]) # Discount betweenness if more than
for w in pred[v]: # one shortest path.
if w in pred:
num_paths = len(pred[w]) # Discount betweenness, mult path
for x in pred[w]:
between[(w, x)] += between[(v, w)] / num_paths
between[(x, w)] += between[(w, v)] / num_paths
return between
def _edge_betweenness(G,source,nodes,cutoff=False):
"""
Edge betweenness helper.
"""
between={}
# get the predecessor data
#(pred,length)=_fast_predecessor(G,source,cutoff=cutoff)
(pred,length)=predecessor(G,source,cutoff=cutoff,return_seen=True)
# order the nodes by path length
onodes = [ nn for dd,nn in sorted( (dist,n) for n,dist in length.iteritems() )]
# intialize betweenness, doesn't account for any edge weights
for u,v in G.edges(nodes):
between[(u,v)]=1.0
between[(v,u)]=1.0
while onodes: # work through all paths
v=onodes.pop()
if v in pred:
num_paths=len(pred[v]) # Discount betweenness if more than
for w in pred[v]: # one shortest path.
if w in pred:
num_paths=len(pred[w]) # Discount betweenness, mult path
for x in pred[w]:
between[(w,x)]+=between[(v,w)]/num_paths
between[(x,w)]+=between[(w,v)]/num_paths
return between
def _node_betweenness(G,
source,
cutoff=False,
normalized=True,
weighted_edges=False):
"""Node betweenness helper:
see betweenness_centrality for what you probably want.
This actually computes "load" and not betweenness.
See https://networkx.lanl.gov/ticket/103
This calculates the load of each node for paths from a single source.
(The fraction of number of shortests paths from source that go
through each node.)
To get the load for a node you need to do all-pairs shortest paths.
If weighted_edges is True then use Dijkstra for finding shortest paths.
In this case a cutoff is not implemented and so is ignored.
"""
# get the predecessor and path length data
if weighted_edges:
(pred, length) = dijkstra_predecessor_and_distance(G, source)
else:
(pred, length) = predecessor(G,
source,
cutoff=cutoff,
return_seen=True)
# order the nodes by path length
onodes = [(l, vert) for (vert, l) in length.items()]
onodes.sort()
onodes[:] = [vert for (l, vert) in onodes if l > 0]
# intialize betweenness
between = {}.fromkeys(length, 1.0)
while onodes:
v = onodes.pop()
if v in pred:
num_paths = len(pred[v]) # Discount betweenness if more than
for x in pred[v]: # one shortest path.
if x == source: # stop if hit source because all remaining v
break # also have pred[v]==[source]
between[x] += between[v] / float(num_paths)
# remove source
for v in between:
between[v] -= 1
# rescale to be between 0 and 1
if normalized:
l = len(between)
if l > 2:
scale = 1.0 / float(
(l - 1) * (l - 2)) # 1/the number of possible paths
for v in between:
between[v] *= scale
return between
def _node_betweenness(G,source,cutoff=False,normalized=True,weighted_edges=False):
"""Node betweenness helper:
see betweenness_centrality for what you probably want.
This actually computes "load" and not betweenness.
See https://networkx.lanl.gov/ticket/103
This calculates the load of each node for paths from a single source.
(The fraction of number of shortests paths from source that go
through each node.)
To get the load for a node you need to do all-pairs shortest paths.
If weighted_edges is True then use Dijkstra for finding shortest paths.
In this case a cutoff is not implemented and so is ignored.
"""
# get the predecessor and path length data
if weighted_edges:
(pred,length)=dijkstra_predecessor_and_distance(G,source)
else:
(pred,length)=predecessor(G,source,cutoff=cutoff,return_seen=True)
# order the nodes by path length
onodes = [ (l,vert) for (vert,l) in length.items() ]
onodes.sort()
onodes[:] = [vert for (l,vert) in onodes if l>0]
# intialize betweenness
between={}.fromkeys(length,1.0)
while onodes:
v=onodes.pop()
if v in pred:
num_paths=len(pred[v]) # Discount betweenness if more than
for x in pred[v]: # one shortest path.
if x==source: # stop if hit source because all remaining v
break # also have pred[v]==[source]
between[x]+=between[v]/float(num_paths)
# remove source
for v in between:
between[v]-=1
# rescale to be between 0 and 1
if normalized:
l=len(between)
if l > 2:
scale=1.0/float((l-1)*(l-2)) # 1/the number of possible paths
for v in between:
between[v] *= scale
return between
