Am 23.03.20 um 22:14 schrieb Franco Peschiera:
> Hello Tiago,
> 
> First of all, thanks for your time.
> 
> I see what you mean by having a biased logic that would prefer shorter
> paths to longer ones, I had not thought about that.
> 
> Regarding the self-reference part, I think it would not be a problem
> because of the structure of my particular (directed) graph. In fact,
> each node represents an assignment *at some given time period* and the
> outward neighbors of a node represent assignments *in the future*. In
> this way, a path can never visit a previously visited node since there
> are no possible cycles. In fact I can easily calculate the shortest and
> longest possible path between two nodes (shortest: using graphql's
> `shortest_distance` method, longest= number of periods in between the
> two nodes).

Well, for DAG (directed acyclic graphs) the situation is quite
different, you should have said so in the beginning.

> So the paths I want to create (or sample) are just the different ways
> one can go from a node N1 (in period P1) to node N2 (in period P2 > P1). 
> I think that in my graph I could just sample neighbors with a weight
> that depends on how far they are (in number of periods) from the node:
> the farthest neighbor will have the least probability of being chosen.
> This way, I'd compensate the fact that shorter paths take less hops.
> 
> What do you think?

Why do I get the impression I'm using google more than you to answer
your question?

Here is an approach using rejection sampling:

https://math.stackexchange.com/questions/2673132/a-procedure-for-sampling-paths-in-a-directed-acyclic-graph

Another approach is to count the number of paths that go through each
node (this is feasible for DAGs) and use this to sample directly, see:

https://pdfs.semanticscholar.org/0d74/e82c41124f83c842d5432abcb914ed1f410f.pdf

Best,
Tiago


-- 
Tiago de Paula Peixoto <[email protected]>
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