graph-traversal

问题 Given a graph G(V, E) , a source vertex s and destination vertex d , the problem is to find all possible paths from s to d where G may contain loops and cycles. I want to get all simple paths, no cycle is allowed. What would be the complexity of this problem? 回答1: This problem is NP-hard, since its output may have an exponential size w.r.t its input. Finding the longest path between two points is already NP-hard (reduction to hamiltonian path problem), so finding all of them is as well. You

问题 I'm trying to get breadth first enumeration working with Gremlin, however I'm having trouble finding a way to output all the steps observed during the enumeration. I can only print out the result of the last iteration. My question would be, given a starting node like this, how can I follow all paths (without knowing the overall depth) using Gremlin and print out everything I find along the way? study=g.v('myId') I have tried the scatter approach, looping approach (although both seem to

问题 Supose I have a class representing a friend and another one representing pairs of best friends and a list of pairs of best friends and want to know each friend that is indirectly related to each one's best friend, as follows: class Friend { string Name; } class BestFriends { Friend NameFriend1; Friend NameFriend2; } List<BestFriends> ListOfBestFriends = new List<BestFriends>(); Supose I have the BestFriends pairs as follows: Adam and Brian; Brian and Chris; Chris and Daniel; Eddie and Ian;

问题 I was wondering if there is an algorithm which: given a fully connected graph of n-nodes (with different weights)... will give me the cheapest cycle to go from node A (a start node) to all other nodes, and return to node A? Is there a way to alter an algorithm like Primm's to accomplish this? Thanks for your help EDIT: I forgot to mention I'm dealing with a undirected graph so the in-degree = out-degree for each vertex. 回答1: Can you not modify Dijkstra, to find you the shortest path to all

问题 I was wondering if there is an algorithm which: given a fully connected graph of n-nodes (with different weights)... will give me the cheapest cycle to go from node A (a start node) to all other nodes, and return to node A? Is there a way to alter an algorithm like Primm's to accomplish this? Thanks for your help EDIT: I forgot to mention I'm dealing with a undirected graph so the in-degree = out-degree for each vertex. 回答1: Can you not modify Dijkstra, to find you the shortest path to all

问题 I was wondering if there is an algorithm which: given a fully connected graph of n-nodes (with different weights)... will give me the cheapest cycle to go from node A (a start node) to all other nodes, and return to node A? Is there a way to alter an algorithm like Primm's to accomplish this? Thanks for your help EDIT: I forgot to mention I'm dealing with a undirected graph so the in-degree = out-degree for each vertex. 回答1: Can you not modify Dijkstra, to find you the shortest path to all

问题 I'm trying to figure out a neat way of traversing a graph Scala-style, preferably with vals and immutable data types. Given the following graph, val graph = Map(0 -> Set(1), 1 -> Set(2), 2 -> Set(0, 3, 4), 3 -> Set(), 4 -> Set(3)) I'd like the output to be the depth first traversal starting in a given node. Starting in 1 for instance, should yield for instance 1 2 3 0 4 . I can't seem to figure out a nice way of doing this without mutable collections or vars. Any help would be appreciated.

问题 I have a graph with three collections which items can be connected by edges. ItemA is a parent of itemB which in turn is a parent of itemC. Elements only can be connected by edges in direction "_from : child, _to : parent" Currently I can get only "linear" result with this AQL query: LET contains = (FOR v IN 1..? INBOUND 'collectionA/itemA' GRAPH 'myGraph' RETURN v) RETURN { "root": { "id": "ItemA", "contains": contains } } And result looks like this: "root": { "id": "itemA", "contains": [ {

问题 I quote from Artificial Intelligence: A Modern Approach: The properties of depth-first search depend strongly on whether the graph-search or tree-search version is used. The graph-search version, which avoids repeated states and redundant paths, is complete in finite state spaces because it will eventually expand every node. The tree-search version, on the other hand, is not complete [...]. Depth-first tree search can be modified at no extra memory cost so that it checks new states against

问题 I know the title is a bit messy, but I don't know how to explain it better. What I'm trying to do: Using a graph found in a text file, find and print the shortest path (minimum amount of vertices) from vertex A to vertex B. Note: using breadth-first search, not Dijkstra's. What I've got: A working algorithm that applies BFS on the graph, but no good way of actually printing out the shortest path. I'm having a hard time distinguishing a vertex in the shortest path from one that is simply run