内容简介:Today, we are going to see how we can root a tree. This is the 8th post of my ongoing seriesThis is one of those very basic and fundamental transformations we need to have if we want to work with rooted trees. The motivation of rooting a tree is that often
Graph Theory | Rooting a Tree
Today, we are going to see how we can root a tree. This is the 8th post of my ongoing series Graph Theory : Go Hero . You should definitely check out the index page to deep dive into Graphs and related problems. I mostly try to come up with new posts of this series in every weekends. Let’s see how rooting is done.
This is one of those very basic and fundamental transformations we need to have if we want to work with rooted trees. The motivation of rooting a tree is that often it can help to add a structure and simplify the problem. A rooted tree can convert an undirected tree into a directed one which lot more easier to work with. Conceptually, rooting a tree is like picking up the tree by a specific node and having all the edges point downwards.
We can root a tree by using any of it’s nodes, however be cautious about the node we are choosing because not all nodes would not generate well balanced trees. So we need to be a bit selective.
In some situations, it’s always a good idea to have a route back to the parent node so that we can walk back. I have illustrated routes to parent nodes with red lines below.
Let’s see how we can root a tree.
Rooting Solution
Rooting a tree is easily done with a Depth First Search ( DFS ). I have created an animated version of the resultant DFS below. You would definitely understand it, for sure.
and that’s rooting a tree in a nutshell.
Pseudo Code
class Treenode: int id; Treenode parent; Treenode [] children;function rootTree(g, rootId = 0): root = Treenode(rootId, null, []) return buildTree(g, root, null)function buildTree(g, node, parent): for child in g[node.id]: if parent != null and childId == parent.id: continue child = Treenode(childId, node, []) node.children.add(child) buildTree(g, child, node) return node
We have a class defined with name Treenode. Every node in the tree would have an unique id, that’s what we are storing in the id placeholder. As we discussed earlier, it’s always a best practice to save the parent node because it would help us to travel back. Also, we save some references to the children of the current node.
Then we define a function called rootTree which takes two parameters into it – a graph and the id of the node to get start. The graph g would be represented as an adjacency list with undirected edges. The first line of rootTree method creates a Treenode object with given rootId , parent reference and list of children. The rootTree function invokes another function named buildTree with paramters graph g, root node and reference to the parent node.
The buildTree method takes the exact three parameters we just talked about. As we enter into the function, we end up landing in a for loop which travels all over the children of the current node. We know the edges are undirected, so we absolutely need to manage the situation where we add a directed edge pointing towards the same node. If the above condition is not met, we are sure that we have a confirmed child in our hand. Then we create an object to the Treenode class and add the child to the list of children of the current node. Afterwards, it does DFS more into the tree using the newly created node. We return the current node as we visit all the neighbors of the node.
So, that’s how we root a tree. We will discuss about Tree center(s) in the coming post. Let’s keep learning, together.
Cheers all.
以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网
猜你喜欢:本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们。
文明之光(第四册)
吴军 / 人民邮电出版社 / 2017-3 / 69.00元
计算机科学家吴军博士继创作《浪潮之巅》、《数学之美》之后,将视角拉回到人类文明史,以他独具的观点从对人类文明产生了重大影响却在过去被忽略的历史故事里,选择了有意思的几十个片段特写,有机地展现了一幅人类文明发展的画卷。《文明之光》系列创作历经整整四年,本书为其第四卷。 作者所选的创作素材来自于十几年来在世界各地的所见所闻,对其内容都有着深刻的体会和认识。《文明之光》系列第四册每个章节依然相对独......一起来看看 《文明之光(第四册)》 这本书的介绍吧!