Shortest path in a directed weighted graph

Question

Suppose we have a directed, weighted graph, $G = (V, E, w)$, with non-negative weights. We define the weight of the shortest path different from the original definition.
The weight of a path with at most 5 edges (including 5) will be defined as usual, meaning the sum of the edges weights. The weight of a path with at least 6 edges will be the sum of the edges weights multiplied by 2.
Propose and analyze an efficient algorithm (as you can), That given a graph $G$ and two vertices $u, v$, the algorithm will find the shortest path from $u$ to $v$, as we defined the paths weights. Describe in words why the algorithm works and analyze it's runtime.
My thought was to run first $BFS$ on the graph and for every edge that in a path with 6 or more edges, just multiply the edge weight by 2, and then run dijkstra, but I think it won't work. So then I was tending to do a brute force algorithm and find all the paths from $u$ to $v$ and then find the shortest one, but it is not efficient. So I'm stuck on an answer to this one and would glad for some help.

Answer 1

You can take this approach:
Step one:
Duplicate G's nodes 6 times to get a new directed graph with 6 levels $L_1,...,L_6.$
For $1\le i\le5$ and nodes $a,b: $ There will be an edge from $a \in L_i$ to $ b \in L_{i+1} $ if there was an edge from $a$ to $b$ in the original graph, and the weights will be same as the original graph.
From $L_6$ there will be no way to move between the nodes, or to move to any other level.
In this way we can run an SP algorithm from node $u$ in $L_1$ to find the shortest path to $v$ (in any level) with respect to $w$, and each path's length will be no longer than 5.
Assume that we got a shortest path $s_1$ (if no path was found $s_1 = \infty).$
Step two:
Create a new weight function $f(w)=2w$ for the original graph's weights and run Dijkstra's again on the original graph with the new weight function to get another shortest path $s_2$ (again if no path was found $s_2 = \infty).$
Step Three:
return $\min\{s_1,s_2\}.$

Time complexity is that of Dijkstra's since constructing the graph in step 1 takes linear time.