Summary - Graph

Print a small graph as an adjacency list in a mini task.

Graph: vertex + edge; cycles allowed. Directed/undirected, weighted/unweighted. Adjacency list O(V+E), matrix O(V^2).

Sparse graph → list, dense → matrix. Edge insertion is efficient in both.

Graphs underpin pathfinding, network analysis, dependency resolution. BFS and DFS work on this structure.

Exercise Summary: count nodes

Write count_nodes(graph). Return the node count. Print 2 with print(count_nodes({'a': ['b'], 'b': ['a']})).

Show example solution

def count_nodes(graph):
    # Count nodes
    return len(graph)
print(count_nodes({'a': ['b'], 'b': ['a']}))

Edge

The connection in a graph is called an ____.

Show answer

The connection in a graph is called an edge.

Next lesson: BFS idea