Find a path from s to t using as few red nodes as possible












4












$begingroup$


Was doing a little interview prep. Given an undirected graph G, such that each node is colored red or blue and |E|≥|V|, find a path in O(|E|) time such that starting and ending at 2 blue nodes, s and t, that you pass through as few red nodes as possible.



Initial Impressions: Since |E|≥|V|, O(|E|) time would include O(|E|+|V|), which means the solution likely uses BFS or DFS. Modifying the graph such that causing the all red nodes must be forced down a directed path of some long length (after making the whole graph directed) in order to use out-of-the-box BFS seems not viable, as it would mean constructing a new graph would be along O(|E||V|) time.



Another method I toyed around with was propagating values to nodes based on the safest path to that node while doing a DFS search, but not all values were guaranteed to update.



I still want to try to solve this myself, but I'm really stuck right now. Was wondering if there were any hints I could get. There are much simpler ways of doing this if it weren't for the O(|E|) time. Djikstras with creating some edge weights would work, but wouldn't be within the time bound.










share|cite|improve this question









$endgroup$








  • 1




    $begingroup$
    Try a variant of BFS in which you first find all red nodes reachable only via blue nodes, and so on.
    $endgroup$
    – Yuval Filmus
    7 hours ago
















4












$begingroup$


Was doing a little interview prep. Given an undirected graph G, such that each node is colored red or blue and |E|≥|V|, find a path in O(|E|) time such that starting and ending at 2 blue nodes, s and t, that you pass through as few red nodes as possible.



Initial Impressions: Since |E|≥|V|, O(|E|) time would include O(|E|+|V|), which means the solution likely uses BFS or DFS. Modifying the graph such that causing the all red nodes must be forced down a directed path of some long length (after making the whole graph directed) in order to use out-of-the-box BFS seems not viable, as it would mean constructing a new graph would be along O(|E||V|) time.



Another method I toyed around with was propagating values to nodes based on the safest path to that node while doing a DFS search, but not all values were guaranteed to update.



I still want to try to solve this myself, but I'm really stuck right now. Was wondering if there were any hints I could get. There are much simpler ways of doing this if it weren't for the O(|E|) time. Djikstras with creating some edge weights would work, but wouldn't be within the time bound.










share|cite|improve this question









$endgroup$








  • 1




    $begingroup$
    Try a variant of BFS in which you first find all red nodes reachable only via blue nodes, and so on.
    $endgroup$
    – Yuval Filmus
    7 hours ago














4












4








4


1



$begingroup$


Was doing a little interview prep. Given an undirected graph G, such that each node is colored red or blue and |E|≥|V|, find a path in O(|E|) time such that starting and ending at 2 blue nodes, s and t, that you pass through as few red nodes as possible.



Initial Impressions: Since |E|≥|V|, O(|E|) time would include O(|E|+|V|), which means the solution likely uses BFS or DFS. Modifying the graph such that causing the all red nodes must be forced down a directed path of some long length (after making the whole graph directed) in order to use out-of-the-box BFS seems not viable, as it would mean constructing a new graph would be along O(|E||V|) time.



Another method I toyed around with was propagating values to nodes based on the safest path to that node while doing a DFS search, but not all values were guaranteed to update.



I still want to try to solve this myself, but I'm really stuck right now. Was wondering if there were any hints I could get. There are much simpler ways of doing this if it weren't for the O(|E|) time. Djikstras with creating some edge weights would work, but wouldn't be within the time bound.










share|cite|improve this question









$endgroup$




Was doing a little interview prep. Given an undirected graph G, such that each node is colored red or blue and |E|≥|V|, find a path in O(|E|) time such that starting and ending at 2 blue nodes, s and t, that you pass through as few red nodes as possible.



Initial Impressions: Since |E|≥|V|, O(|E|) time would include O(|E|+|V|), which means the solution likely uses BFS or DFS. Modifying the graph such that causing the all red nodes must be forced down a directed path of some long length (after making the whole graph directed) in order to use out-of-the-box BFS seems not viable, as it would mean constructing a new graph would be along O(|E||V|) time.



Another method I toyed around with was propagating values to nodes based on the safest path to that node while doing a DFS search, but not all values were guaranteed to update.



I still want to try to solve this myself, but I'm really stuck right now. Was wondering if there were any hints I could get. There are much simpler ways of doing this if it weren't for the O(|E|) time. Djikstras with creating some edge weights would work, but wouldn't be within the time bound.







graphs






share|cite|improve this question













share|cite|improve this question











share|cite|improve this question




share|cite|improve this question










asked 9 hours ago









Hunter DyerHunter Dyer

384




384








  • 1




    $begingroup$
    Try a variant of BFS in which you first find all red nodes reachable only via blue nodes, and so on.
    $endgroup$
    – Yuval Filmus
    7 hours ago














  • 1




    $begingroup$
    Try a variant of BFS in which you first find all red nodes reachable only via blue nodes, and so on.
    $endgroup$
    – Yuval Filmus
    7 hours ago








1




1




$begingroup$
Try a variant of BFS in which you first find all red nodes reachable only via blue nodes, and so on.
$endgroup$
– Yuval Filmus
7 hours ago




$begingroup$
Try a variant of BFS in which you first find all red nodes reachable only via blue nodes, and so on.
$endgroup$
– Yuval Filmus
7 hours ago










2 Answers
2






active

oldest

votes


















4












$begingroup$

To solve this, you need to use $BFS$. But first, manipulate $G$ so that the path will always favor blue vertices.



The solution has 2 parts:




  1. Use $DFS$ on blue vertices only, to find all all-blue strongly connected components, or $SCC$. Let's denote each $SCC$ by $v'$. Now, each blue $v in v'$ will be "compressed" to a single vertex $u$, and an edge $(u,x)$ will be added for every $x in N(v')$.
    Note any such $x$ is necessarily red.
    This step costs $O(V+E) = O(E)$, since $DFS$ is $O(V+E)$, and you have at most $V$ blue vertices, which make no more than $E$ new edges to add.


Step 1 means all paths that are blue-only will be free. On the new graph, the $BFS$ will only consider the edges which pass through a red vertex.




  1. Use $BFS$ from $s$. That length of the path to $t$ will essentially be the shortest path under the constraint of least red vertices in the path.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    the graph is undirected, so step 1 should be to find all all-blue connected components, not SCCs, right?
    $endgroup$
    – Kevin Wang
    4 hours ago










  • $begingroup$
    I believe that is a typo by lox. Yes, it should be connected components instead of SCC.
    $endgroup$
    – Apass.Jack
    4 hours ago



















1












$begingroup$

Convert $G$ to a directed graph $G'$ where we have two edges $(u,v)$ and $(v,u)$ in $G'$ for every edge ${u,v}$ in $G$. Let the length of $(u,v)$ be 1 if $v$ is a red node and 0 otherwise. Now run Dijkstra's algorithm on $G'$ from the starting node $s$ to the ending node $t$.





It is clear that the shortest path thus found passes as few red nodes as possible.



While we are running Dijkstra's algorithm, we are in one of two kinds of stages alternatively. One kind of stage is when we are exploring towards red nodes. The other kind of stage is when we are exploring towards blue nodes. Since each edge is checked/visited as most twice, the running time is $O(|E|)$.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    Yeah that definitely works, but the runtime of Dijkstra's is O(|E| + |V|log|V|) which is more than O(|E|).
    $endgroup$
    – Hunter Dyer
    5 hours ago










  • $begingroup$
    I will show shortly that this particular run of Dijkstra's algorithm actually takes $O(|E|)$ time.
    $endgroup$
    – Apass.Jack
    4 hours ago












  • $begingroup$
    What is nice about this simple kind of transformation is that it also works when we are dealing with a weighted graph. Just add a sufficiently large weight to edges that end with a red node. Also this algorithm seems significantly easier to code as well.
    $endgroup$
    – Apass.Jack
    3 hours ago














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2 Answers
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active

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2 Answers
2






active

oldest

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active

oldest

votes






active

oldest

votes









4












$begingroup$

To solve this, you need to use $BFS$. But first, manipulate $G$ so that the path will always favor blue vertices.



The solution has 2 parts:




  1. Use $DFS$ on blue vertices only, to find all all-blue strongly connected components, or $SCC$. Let's denote each $SCC$ by $v'$. Now, each blue $v in v'$ will be "compressed" to a single vertex $u$, and an edge $(u,x)$ will be added for every $x in N(v')$.
    Note any such $x$ is necessarily red.
    This step costs $O(V+E) = O(E)$, since $DFS$ is $O(V+E)$, and you have at most $V$ blue vertices, which make no more than $E$ new edges to add.


Step 1 means all paths that are blue-only will be free. On the new graph, the $BFS$ will only consider the edges which pass through a red vertex.




  1. Use $BFS$ from $s$. That length of the path to $t$ will essentially be the shortest path under the constraint of least red vertices in the path.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    the graph is undirected, so step 1 should be to find all all-blue connected components, not SCCs, right?
    $endgroup$
    – Kevin Wang
    4 hours ago










  • $begingroup$
    I believe that is a typo by lox. Yes, it should be connected components instead of SCC.
    $endgroup$
    – Apass.Jack
    4 hours ago
















4












$begingroup$

To solve this, you need to use $BFS$. But first, manipulate $G$ so that the path will always favor blue vertices.



The solution has 2 parts:




  1. Use $DFS$ on blue vertices only, to find all all-blue strongly connected components, or $SCC$. Let's denote each $SCC$ by $v'$. Now, each blue $v in v'$ will be "compressed" to a single vertex $u$, and an edge $(u,x)$ will be added for every $x in N(v')$.
    Note any such $x$ is necessarily red.
    This step costs $O(V+E) = O(E)$, since $DFS$ is $O(V+E)$, and you have at most $V$ blue vertices, which make no more than $E$ new edges to add.


Step 1 means all paths that are blue-only will be free. On the new graph, the $BFS$ will only consider the edges which pass through a red vertex.




  1. Use $BFS$ from $s$. That length of the path to $t$ will essentially be the shortest path under the constraint of least red vertices in the path.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    the graph is undirected, so step 1 should be to find all all-blue connected components, not SCCs, right?
    $endgroup$
    – Kevin Wang
    4 hours ago










  • $begingroup$
    I believe that is a typo by lox. Yes, it should be connected components instead of SCC.
    $endgroup$
    – Apass.Jack
    4 hours ago














4












4








4





$begingroup$

To solve this, you need to use $BFS$. But first, manipulate $G$ so that the path will always favor blue vertices.



The solution has 2 parts:




  1. Use $DFS$ on blue vertices only, to find all all-blue strongly connected components, or $SCC$. Let's denote each $SCC$ by $v'$. Now, each blue $v in v'$ will be "compressed" to a single vertex $u$, and an edge $(u,x)$ will be added for every $x in N(v')$.
    Note any such $x$ is necessarily red.
    This step costs $O(V+E) = O(E)$, since $DFS$ is $O(V+E)$, and you have at most $V$ blue vertices, which make no more than $E$ new edges to add.


Step 1 means all paths that are blue-only will be free. On the new graph, the $BFS$ will only consider the edges which pass through a red vertex.




  1. Use $BFS$ from $s$. That length of the path to $t$ will essentially be the shortest path under the constraint of least red vertices in the path.






share|cite|improve this answer











$endgroup$



To solve this, you need to use $BFS$. But first, manipulate $G$ so that the path will always favor blue vertices.



The solution has 2 parts:




  1. Use $DFS$ on blue vertices only, to find all all-blue strongly connected components, or $SCC$. Let's denote each $SCC$ by $v'$. Now, each blue $v in v'$ will be "compressed" to a single vertex $u$, and an edge $(u,x)$ will be added for every $x in N(v')$.
    Note any such $x$ is necessarily red.
    This step costs $O(V+E) = O(E)$, since $DFS$ is $O(V+E)$, and you have at most $V$ blue vertices, which make no more than $E$ new edges to add.


Step 1 means all paths that are blue-only will be free. On the new graph, the $BFS$ will only consider the edges which pass through a red vertex.




  1. Use $BFS$ from $s$. That length of the path to $t$ will essentially be the shortest path under the constraint of least red vertices in the path.







share|cite|improve this answer














share|cite|improve this answer



share|cite|improve this answer








edited 4 hours ago









templatetypedef

5,59911945




5,59911945










answered 6 hours ago









loxlox

1866




1866












  • $begingroup$
    the graph is undirected, so step 1 should be to find all all-blue connected components, not SCCs, right?
    $endgroup$
    – Kevin Wang
    4 hours ago










  • $begingroup$
    I believe that is a typo by lox. Yes, it should be connected components instead of SCC.
    $endgroup$
    – Apass.Jack
    4 hours ago


















  • $begingroup$
    the graph is undirected, so step 1 should be to find all all-blue connected components, not SCCs, right?
    $endgroup$
    – Kevin Wang
    4 hours ago










  • $begingroup$
    I believe that is a typo by lox. Yes, it should be connected components instead of SCC.
    $endgroup$
    – Apass.Jack
    4 hours ago
















$begingroup$
the graph is undirected, so step 1 should be to find all all-blue connected components, not SCCs, right?
$endgroup$
– Kevin Wang
4 hours ago




$begingroup$
the graph is undirected, so step 1 should be to find all all-blue connected components, not SCCs, right?
$endgroup$
– Kevin Wang
4 hours ago












$begingroup$
I believe that is a typo by lox. Yes, it should be connected components instead of SCC.
$endgroup$
– Apass.Jack
4 hours ago




$begingroup$
I believe that is a typo by lox. Yes, it should be connected components instead of SCC.
$endgroup$
– Apass.Jack
4 hours ago











1












$begingroup$

Convert $G$ to a directed graph $G'$ where we have two edges $(u,v)$ and $(v,u)$ in $G'$ for every edge ${u,v}$ in $G$. Let the length of $(u,v)$ be 1 if $v$ is a red node and 0 otherwise. Now run Dijkstra's algorithm on $G'$ from the starting node $s$ to the ending node $t$.





It is clear that the shortest path thus found passes as few red nodes as possible.



While we are running Dijkstra's algorithm, we are in one of two kinds of stages alternatively. One kind of stage is when we are exploring towards red nodes. The other kind of stage is when we are exploring towards blue nodes. Since each edge is checked/visited as most twice, the running time is $O(|E|)$.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    Yeah that definitely works, but the runtime of Dijkstra's is O(|E| + |V|log|V|) which is more than O(|E|).
    $endgroup$
    – Hunter Dyer
    5 hours ago










  • $begingroup$
    I will show shortly that this particular run of Dijkstra's algorithm actually takes $O(|E|)$ time.
    $endgroup$
    – Apass.Jack
    4 hours ago












  • $begingroup$
    What is nice about this simple kind of transformation is that it also works when we are dealing with a weighted graph. Just add a sufficiently large weight to edges that end with a red node. Also this algorithm seems significantly easier to code as well.
    $endgroup$
    – Apass.Jack
    3 hours ago


















1












$begingroup$

Convert $G$ to a directed graph $G'$ where we have two edges $(u,v)$ and $(v,u)$ in $G'$ for every edge ${u,v}$ in $G$. Let the length of $(u,v)$ be 1 if $v$ is a red node and 0 otherwise. Now run Dijkstra's algorithm on $G'$ from the starting node $s$ to the ending node $t$.





It is clear that the shortest path thus found passes as few red nodes as possible.



While we are running Dijkstra's algorithm, we are in one of two kinds of stages alternatively. One kind of stage is when we are exploring towards red nodes. The other kind of stage is when we are exploring towards blue nodes. Since each edge is checked/visited as most twice, the running time is $O(|E|)$.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    Yeah that definitely works, but the runtime of Dijkstra's is O(|E| + |V|log|V|) which is more than O(|E|).
    $endgroup$
    – Hunter Dyer
    5 hours ago










  • $begingroup$
    I will show shortly that this particular run of Dijkstra's algorithm actually takes $O(|E|)$ time.
    $endgroup$
    – Apass.Jack
    4 hours ago












  • $begingroup$
    What is nice about this simple kind of transformation is that it also works when we are dealing with a weighted graph. Just add a sufficiently large weight to edges that end with a red node. Also this algorithm seems significantly easier to code as well.
    $endgroup$
    – Apass.Jack
    3 hours ago
















1












1








1





$begingroup$

Convert $G$ to a directed graph $G'$ where we have two edges $(u,v)$ and $(v,u)$ in $G'$ for every edge ${u,v}$ in $G$. Let the length of $(u,v)$ be 1 if $v$ is a red node and 0 otherwise. Now run Dijkstra's algorithm on $G'$ from the starting node $s$ to the ending node $t$.





It is clear that the shortest path thus found passes as few red nodes as possible.



While we are running Dijkstra's algorithm, we are in one of two kinds of stages alternatively. One kind of stage is when we are exploring towards red nodes. The other kind of stage is when we are exploring towards blue nodes. Since each edge is checked/visited as most twice, the running time is $O(|E|)$.






share|cite|improve this answer











$endgroup$



Convert $G$ to a directed graph $G'$ where we have two edges $(u,v)$ and $(v,u)$ in $G'$ for every edge ${u,v}$ in $G$. Let the length of $(u,v)$ be 1 if $v$ is a red node and 0 otherwise. Now run Dijkstra's algorithm on $G'$ from the starting node $s$ to the ending node $t$.





It is clear that the shortest path thus found passes as few red nodes as possible.



While we are running Dijkstra's algorithm, we are in one of two kinds of stages alternatively. One kind of stage is when we are exploring towards red nodes. The other kind of stage is when we are exploring towards blue nodes. Since each edge is checked/visited as most twice, the running time is $O(|E|)$.







share|cite|improve this answer














share|cite|improve this answer



share|cite|improve this answer








edited 4 hours ago

























answered 5 hours ago









Apass.JackApass.Jack

13.7k1940




13.7k1940












  • $begingroup$
    Yeah that definitely works, but the runtime of Dijkstra's is O(|E| + |V|log|V|) which is more than O(|E|).
    $endgroup$
    – Hunter Dyer
    5 hours ago










  • $begingroup$
    I will show shortly that this particular run of Dijkstra's algorithm actually takes $O(|E|)$ time.
    $endgroup$
    – Apass.Jack
    4 hours ago












  • $begingroup$
    What is nice about this simple kind of transformation is that it also works when we are dealing with a weighted graph. Just add a sufficiently large weight to edges that end with a red node. Also this algorithm seems significantly easier to code as well.
    $endgroup$
    – Apass.Jack
    3 hours ago




















  • $begingroup$
    Yeah that definitely works, but the runtime of Dijkstra's is O(|E| + |V|log|V|) which is more than O(|E|).
    $endgroup$
    – Hunter Dyer
    5 hours ago










  • $begingroup$
    I will show shortly that this particular run of Dijkstra's algorithm actually takes $O(|E|)$ time.
    $endgroup$
    – Apass.Jack
    4 hours ago












  • $begingroup$
    What is nice about this simple kind of transformation is that it also works when we are dealing with a weighted graph. Just add a sufficiently large weight to edges that end with a red node. Also this algorithm seems significantly easier to code as well.
    $endgroup$
    – Apass.Jack
    3 hours ago


















$begingroup$
Yeah that definitely works, but the runtime of Dijkstra's is O(|E| + |V|log|V|) which is more than O(|E|).
$endgroup$
– Hunter Dyer
5 hours ago




$begingroup$
Yeah that definitely works, but the runtime of Dijkstra's is O(|E| + |V|log|V|) which is more than O(|E|).
$endgroup$
– Hunter Dyer
5 hours ago












$begingroup$
I will show shortly that this particular run of Dijkstra's algorithm actually takes $O(|E|)$ time.
$endgroup$
– Apass.Jack
4 hours ago






$begingroup$
I will show shortly that this particular run of Dijkstra's algorithm actually takes $O(|E|)$ time.
$endgroup$
– Apass.Jack
4 hours ago














$begingroup$
What is nice about this simple kind of transformation is that it also works when we are dealing with a weighted graph. Just add a sufficiently large weight to edges that end with a red node. Also this algorithm seems significantly easier to code as well.
$endgroup$
– Apass.Jack
3 hours ago






$begingroup$
What is nice about this simple kind of transformation is that it also works when we are dealing with a weighted graph. Just add a sufficiently large weight to edges that end with a red node. Also this algorithm seems significantly easier to code as well.
$endgroup$
– Apass.Jack
3 hours ago




















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