Some "abstract" real-world graphs (such as social networks, WWW networks, call graphs, scientific collaboration graphs; and not road networks or power grid networks) empirically seem to have a small [hyperbolicity](https://en.wikipedia.org/wiki/Hyperbolic_metric_space).
This property can be used to approximate the input graph by a tree. The tree can, in turn, be used to approximate efficiently some quantities such as the distance between two nodes.
"The key point here is that in a hyperbolic graph selecting the root in the hyperbolic core ensures that it is already at the intersection of O(N) shortest paths"
Question: How can I select a root node efficiently?
Question: Is there any guaranty on the obtained approximation?
Question: How can I compute the hyperbolicity of a graph efficiently?
Not sure why there is "dynamic" in the title.

Some "abstract" real-world graphs (such as social networks, WWW networks, call graphs, scientific collaboration graphs; and not road networks or power grid networks) empirically seem to have a small [hyperbolicity](https://en.wikipedia.org/wiki/Hyperbolic_metric_space).
This property can be used to approximate the input graph by a tree. The tree can, in turn, be used to approximate efficiently some quantities such as the distance between two nodes.
"The key point here is that in a hyperbolic graph selecting the root in the hyperbolic core ensures that it is already at the intersection of O(N) shortest paths"
Question: How can I select a root node efficiently?
Question: Is there any guaranty on the obtained approximation?
Question: How can I compute the hyperbolicity of a graph efficiently?
Not sure why there is "dynamic" in the title.

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