On 11.09.2015 19:15, Christopher Morris wrote:
> Thanks for the quick response.
>
> dist = shortest_distance(g, source=v)
> k_disk = GraphView(g, vfilt=dist.fa <= k)
>
> This will work. But doesn't it first compute the shortest distance
> from v to all other vertices in g and then applies a fil
Thanks for the quick response.
dist = shortest_distance(g, source=v)
k_disk = GraphView(g, vfilt=dist.fa <= k)
This will work. But doesn't it first compute the shortest distance from
v to all other vertices in g and then applies a filter? This is rather
inefficient, especially when then gr
On 11.09.2015 13:59, Christopher Morris wrote:
> Hello,
>
> given an undirected, unweighted graph and a vertex v, I want to
> compute the k-disk around v, i.e. the induced subgraph of vertices at
> a distanst at most k from v.
>
> Of course, this can be easily be done by a variation of BFS or DFS.
Hello,
given an undirected, unweighted graph and a vertex v, I want to compute
the k-disk around v, i.e. the induced subgraph of vertices at a distanst
at most k from v.
Of course, this can be easily be done by a variation of BFS or DFS. Is
it possible do use graph_tool.search.bfs_search in
