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A node is a basic unit of a data structure, such as a linked list or tree data structure. Nodes contain data and also may link to other nodes. Links between nodes are often implemented by pointers. In graph theory, the image provides a simplified view of a network, where each of the numbers represents a different node.
If the element is in a 2-node leaf, just make the adjustments below. Make the following adjustments when a 2-node – except the root node – is encountered on the way to the leaf we want to remove: If a sibling on either side of this node is a 3-node or a 4-node (thus having more than 1 key), perform a rotation with that sibling:
A node is a structure which may contain data and connections to other nodes, sometimes called edges or links. Each node in a tree has zero or more child nodes, which are below it in the tree (by convention, trees are drawn with descendants going downwards). A node that has a child is called the child's parent node (or superior).
An adjacency list representation for a graph associates each vertex in the graph with the collection of its neighbouring vertices or edges. There are many variations of this basic idea, differing in the details of how they implement the association between vertices and collections, in how they implement the collections, in whether they include both vertices and edges or only vertices as first ...
record Node { data; // The data being stored in the node Node next // A reference [2] to the next node, null for last node } record List { Node firstNode // points to first node of list; null for empty list} Traversal of a singly linked list is simple, beginning at the first node and following each next link until reaching the end:
Traversing a tree involves iterating over all nodes in some manner. Because from a given node there is more than one possible next node (it is not a linear data structure), then, assuming sequential computation (not parallel), some nodes must be deferred—stored in some way for later visiting. This is often done via a stack (LIFO) or queue (FIFO).
Because those nodes may also be less than half full, to re-establish the normal B-tree rules, combine such nodes with their (guaranteed full) left siblings and divide the keys to produce two nodes at least half full. The only node which lacks a full left sibling is the root, which is permitted to be less than half full.
The purpose of the delete algorithm is to remove the desired entry node from the tree structure. We recursively call the delete algorithm on the appropriate node until no node is found. For each function call, we traverse along, using the index to navigate until we find the node, remove it, and then work back up to the root.