Its a reference to visualize some of core concepts of Trees and Graphs data structures
The Depth of a node
1. Is the number of edges from the node to the tree's root node
2. Root node will have a Depth of 0
The Height of a node
1. Is the number of edges on the longest path from the node to a leaf
2. Leaf node(s) will have a Height of 0
In terms of programming, there are two common ways to represent a graph.
1. By Adjacency List
2. By Adjacency Matrix
PreOrderTravesal
1. [[ traverse current node ]]
2. traverse left tree
3. traverse right tree
InOrderTravesal
1. traverse left tree
2. [[ traverse current node ]]
3. traverse right tree
PostOrderTravesal
1. traverse left tree
2. traverse right tree
3. [[ traverse current node ]]
For simplicity, we will visualize over Trees as Trees are special subtype of Graghs
Trees are already Graphs but without CYCLES
DFS (Depth First Search)
1. [[ traverse current node ]]
2. [[ mark current node as visited ]]
3. For Each NON-Visited neighbors
=> visit it (Recursion)
Note: DFS can be also implemented by Stack (Iterative)
BFS (Breadth First Search)
1. Enqueue root node into a Queue
2. While Queue is not empty => (Iterative)
2.1 Dequeue Queue - current node
2.2 [[ traverse current node ]]
2.3 [[ mark current node as visited ]]
2.4 For Each NON-Visited neighbors
=> Enqueue into Queue
