Graph theorists use the following definition. A binary tree is a connected acyclic graph such that the degree of each vertex is no more than 3. A rooted binary tree is such a graph that has one of its vertices of degree no more than 2 singled out as the root. With the root thus chosen, each vertex will have a uniquely defined parent, and up to two children; however, so far there is insufficient information to distinguish a left or right child. If we drop the connectedness requirement, and there are multiple connected component in the graph, we call such a structure a forest.
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2 Methods of iterating over binary trees 3 Encoding n-ary trees as binary trees |
Binary trees can be constructed from programming language primitives in several ways. In a language with structures and either pointers or references, binary trees are constructed by having a tree node structure which contains some data and pointers or references to its left child and its right child. The order this imposes on the children is sometimes useful, particularly for in-order traversal. Sometimes it also contains a pointer to its unique parent. If a node has less than two children, some of the child pointers may be set to a special null value, or to a special sentinel node.
Binary trees can also be stored in arrays, and if the tree is a complete binary tree, this method wastes no space, and the resulting data structure is called a heap. In this compact arrangement, a node has an index i, and (assuming zero-based arrays) its children are found at indices 2i+1 and 2i+2, while its parent (if any) is found at index floor(i/2). This method benefits from more compact storage and better locality of reference, particularly during a preorder traversal.
In languages with tagged unions such as ML, a tree node is often a tagged union of two types of nodes, one of which is a 3-tuple of data, left child, and right child, and the other of which is a "leaf" node, which contains no data and functions much like the null value in a language with pointers.
Often, one wishes to visit each of the nodes in a tree and examine the value there. There are several common orders in which the nodes can be visited, and each has useful properties that are exploited in algorithms based on binary trees.
Say we have a structure
This prints the values in the tree in pre-order. In pre-order, each node is visited before any of its children. Similarly, if the print statement were last, each node would be visited after all of its children, and the values would be printed in post-order.
Finally, an in-order traversal, as above, visits each node between the nodes in its left subtree and the nodes in its right subtree. This is a particularly common way of traversing a binary search tree, because it gives the values in increasing order.
In depth-first order, we always attempt to visit the node farthest from the root that we can, but with the caveat that it must be a child of a node we have already visited.
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Contrasting with depth-first order is breadth-first order, which always attempts to visit the node closest to the root that it has not already visited.
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There is a one-to-one mapping between general ordered trees and binary trees, which in particular is used by Lisp to represent general ordered trees as binary trees. Each node N in the ordered tree corresponds to a node N' in the binary tree; the left child of N' is the node corresponding to the first child of N, and the right child of N' is the node corresponding to the N's next sibling --- that is, the next node in order among the children of the parent of N
One way of thinking about this is that each node's children are in a linked list, chained together with their right fields, and the node only has a pointer to the beginning or head of this list, through its left field.
For example, in the tree on the left, A has the 6 children {B,C,D,E,F,G}. It can be converted into the binary tree on the right.
The binary tree can be thought of as the original tree tilted sideways, with the black left edges representing first child and the blue right edges representing next sibling. The leaves of the tree on the left would be written in Lisp as:
See also: AVL tree, B-tree, binary space partition, red-black tree\n
Methods for storing binary trees
Methods of iterating over binary trees
Pre-order, in-order, and post-order traversal
TreeNode which contains a value value and references left and right to its two children. Then we can write the following function:
visit(TreeNode node) {
print node.value
if node.left != null then visit(node.left)
if node.right != null then visit(node.right)
}
visit(TreeNode node) {
if node.left != null then visit(node.left)
print node.value
if node.right != null then visit(node.right)
}
Depth-first order
Breadth-first order
Encoding n-ary trees as binary trees
which would be implemented in memory as the binary tree on the right, without any letters on those nodes that have a left child.