Let G be a graph. Prove that if the degree of every vertex is at least two, that each component of G contains a cycle, that is the number of cycles is greater than or equal to the number of components.
Let G be a graph. Prove that if the degree of every vertex is at least two, that each component of G contains a cycle, that is the number of cycles is greater than or equal to the number of components.
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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
Transcribed Image Text:**Title:** Understanding Graph Theory: Cycles and Components
**Content:**
Let \( G \) be a graph. Prove that if the degree of every vertex is at least two, then each component of \( G \) contains a cycle. This means that the number of cycles is greater than or equal to the number of components.
**Explanation:**
This statement addresses concepts in graph theory, focusing on conditions under which a graph contains cycles. A graph component is a subgraph where any two vertices are connected to each other by paths, and there are no connections to vertices outside the component.
**Key Points:**
1. **Vertex Degree:** The degree of a vertex in a graph is the number of edges connected to it. Here, each vertex having a degree of at least two is crucial.
2. **Cycle in a Graph:** A cycle is a path of edges and vertices wherein a vertex is reachable from itself. For this case, there should be at least one cycle in each component.
3. **Components:** A graph can have multiple components (disconnected subgraphs). The claim is that if each vertex in the entire graph has a degree of at least two, then each of these components will contain a cycle.
**Mathematical Implication:**
The statement implies that under the given degree condition, a component cannot be a tree, because:
- Trees are acyclic connected graphs where every n-node tree has n-1 edges.
- If a component were a tree (no cycles), some vertices would end up with a degree of less than two, contradicting the initial condition.
**Conclusion:**
This theorem identifies a fundamental property of graphs. It ensures that the condition of vertex degrees leads inherently to cyclic structures, enhancing our understanding of graph connectivity and structure.
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