minimum-spanning-tree

问题 How i can calculate im R(3.0.0 - Linux x32) minimum spanning tree with Kruskal's algorithm? I create an weighted full graph with igraph (0.6.5) library as folws: set.seed(1234567890) g <- graph.full(n = 20) E(g)$weight <- round(runif(ecount(g)), 2) * 100 And i am able to calcutae the minimum spaning tree with Prim (igraph) mstPrim <- minimum.spanning.tree(g, algorithm = "prim") But unfortunaly doesn't in "igraph" Kruskal's algorithm implemented. I can represent my genereted graph as a data

问题 How i can calculate im R(3.0.0 - Linux x32) minimum spanning tree with Kruskal's algorithm? I create an weighted full graph with igraph (0.6.5) library as folws: set.seed(1234567890) g <- graph.full(n = 20) E(g)$weight <- round(runif(ecount(g)), 2) * 100 And i am able to calcutae the minimum spaning tree with Prim (igraph) mstPrim <- minimum.spanning.tree(g, algorithm = "prim") But unfortunaly doesn't in "igraph" Kruskal's algorithm implemented. I can represent my genereted graph as a data

问题 Given a graph G,which are the sufficient and necessary conditions , so that this graph has a unique minimum spanning tree?In addition , how can I proove these conditions? So far , I had found that those conditions are : 1)For every partition of V(G) into two subsets, the minimum weight edge with one endpoint in each subset is unique. 2)The maximum-weight edge in any cycle of G is unique. But I am not sure if this is correct.Even in case this is correct,I cannot prove its correctness. 回答1:

问题 My notes tell me that the first and last is false. I need some idea of how to understand the validity of these sentences in a more simple and concise manner. Suppose M is an MST (minimum spanning tree) of the Weighted Graph - GR. Let A be a vertex of GR then M-{A} is also MST of GR-{A}. Let A be a leaf of M then M-{A} is also MST of GR-{A}. If e is a edge of M then (M-{e}) is a forest of M1 and M2 trees such that for M_i, i=1,2 is a MST of Induced Graph GR on vertexes T_i. 来源： https:/

来源： https://stackoverflow.com/questions/17450619/algorithm-to-find-mst-in-a-huge-complete-graph

来源： https://stackoverflow.com/questions/17450619/algorithm-to-find-mst-in-a-huge-complete-graph

问题 I have a directed graph G in networkx and I want to get the minimum spanning tree of it. I do: T = nx.algorithms.minimum_spanning_tree( G.to_undirected() ) This is undirected and I would like to restore the directions but I don't know how to do it. I tried: G[T.edges()] This last line looks very pythonic but this is not the way networkx works, apparently... Does anyone knows how to do it? In other words: How can I get the subgraph of a directed tree given the (undirected) edges ? 回答1: You can

问题 I have a directed graph G in networkx and I want to get the minimum spanning tree of it. I do: T = nx.algorithms.minimum_spanning_tree( G.to_undirected() ) This is undirected and I would like to restore the directions but I don't know how to do it. I tried: G[T.edges()] This last line looks very pythonic but this is not the way networkx works, apparently... Does anyone knows how to do it? In other words: How can I get the subgraph of a directed tree given the (undirected) edges ? 回答1: You can

问题 I can write both Prim's and Kruskal's algorithms to find a minimum spanning tree in C++ or Java, but I want to know how to implement them in Haskell with O(mlogm) or O(mlogn) (pure functional programs is better). Thanks a lot. 回答1: As svenningsson suggests, priority search queue is well suited for both Kruskal's and Prim's (atleast the author proclaims it in his paper.) The problem with Kruskal is that it requires that you have an O(log n) union-find algorithm. A union-find datastructure with

问题 I want to make a dynamic minimum spanning tree. I have an existing MS tree over n vertices and I add one more vertex and edges to all the existing vertices from this new vertex. How can I update the MST for the new graph efficiently? O(n) would be optimal. Can I also make delete vertex operation efficient? 回答1: O(n log n) using Kruskal's algorithm. The key idea is any edges not used in the original MST will not be used in the new MST either. So just sort the n new edges O(n log n) , merge