Computer Science
Grade 10
20 min
Trees and Graphs
Trees and Graphs
Tutorial Preview
1
Introduction & Learning Objectives
Learning Objectives
Define the terms node, edge, root, and leaf in the context of tree data structures.
Differentiate between a tree and a general graph, specifically by identifying cycles.
Explain the hierarchical relationship between parent, child, and sibling nodes in a tree.
Identify real-world scenarios that can be modeled using trees and graphs.
Trace a Breadth-First Search (BFS) traversal on a simple graph.
Trace a Depth-First Search (DFS) traversal on a simple tree.
Ever wonder how a social network knows who your 'friends of friends' are, or how your computer organizes files in folders? 📂 You're about to learn the secret structure behind it all!
This lesson introduces two powerful data structures: Trees and Graphs. You will learn their basic componen...
2
Key Concepts & Vocabulary
TermDefinitionExample
Node (or Vertex)A fundamental part of a tree or graph that holds data. Think of it as a single point or item.In a social network graph, each person's profile is a node.
EdgeA connection between two nodes. It represents a relationship between them.In a social network graph, the 'friendship' connection between two people is an edge.
TreeA special type of graph that has no cycles and is connected. It represents a hierarchy.A family tree, where each person is a node and the parent-child relationships are edges.
RootThe topmost node in a tree, from which all other nodes descend. A tree has exactly one root.In a computer's file system, the 'C:' drive is the root of the directory tree.
LeafA node in a tree that has no children. It is at the end...
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Core Syntax & Patterns
Tree Structure Rule
A connected graph with N nodes is a tree if and only if it has exactly N-1 edges.
Use this rule to quickly determine if a given structure is a tree. If it has N nodes but more or less than N-1 edges, or if it has a cycle, it is not a tree.
Breadth-First Search (BFS) Traversal Pattern
1. Start at a node, add it to a queue, and mark it as visited. 2. While the queue is not empty: Dequeue a node, visit it, and enqueue all its unvisited neighbors, marking them as visited.
BFS explores a graph layer by layer. It's useful for finding the shortest path in an unweighted graph. It uses a Queue data structure (First-In, First-Out).
Depth-First Search (DFS) Traversal Pattern
1. Start at a node, visit it, and mark it as visited. 2. For each of its unvisite...
4 more steps in this tutorial
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Challenging
A graph has N nodes and N-1 edges, but it is NOT a tree. Based on the definitions in the tutorial, what must be true about this graph?
A.The graph is not connected.
B.The graph must have more than one root.
C.The graph must be a single straight line.
D.The graph has N-1 cycles.
Challenging
The BFS traversal of a graph starting from node P is: P, Q, R, S. What can you definitively conclude about the graph's structure?
A.S is a child of R.
B.Q and R are siblings.
C.Q and R are direct neighbors of P.
D.The graph is a tree.
Challenging
A social network is modeled as a graph where users are nodes and friendships are edges. Why is a tree an inappropriate data structure for this model?
A.Because social networks cannot have a root user.
B.Because mutual friendships create cycles (e.g., A is friends with B, B is friends with C, and C is friends with A).
C.Because some users have no friends (leaf nodes).
D.Because the number of users is not equal to the number of friendships minus one.
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