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How do we describe a graph precisely and record it as a matrix?

Use vertex, edge, degree, loop and multiple-edge terminology, apply the handshake rule, and represent a graph with an adjacency matrix.

How to use the core graph vocabulary of vertices, edges, degree, loops and multiple edges, apply the handshake rule, and translate between a graph and its adjacency matrix.

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  1. What this dot point is asking
  2. Core terminology
  3. The adjacency matrix
  4. Why the matrix matters

What this dot point is asking

You must use the vocabulary precisely, apply the handshake rule to check or complete a graph, and convert between a drawn graph and its adjacency matrix.

Core terminology

A graph is a set of vertices (nodes) joined by edges (lines). Key terms:

  • Degree of a vertex: the number of edge ends meeting at it.
  • Loop: an edge joining a vertex to itself; it adds 22 to that vertex's degree.
  • Multiple edges: two or more edges joining the same pair of vertices.
  • Simple graph: a graph with no loops and no multiple edges.
  • Connected graph: one in which you can travel between any two vertices.

The adjacency matrix

An adjacency matrix is a square table with one row and one column per vertex. Each entry records the number of edges directly joining that pair.

  • For an undirected graph the matrix is symmetric (entry from A to B equals entry from B to A).
  • A loop is recorded as a 22 on the diagonal, matching its contribution of 22 to the degree.
  • The degree of a vertex is the sum of its row (counting a diagonal loop entry once as written).

Why the matrix matters

The adjacency matrix lets you store a network for computation, check the degree sequence quickly, and (by matrix multiplication, met later) count walks of a given length. For directed graphs the matrix is generally not symmetric, which is the first sign that direction matters.