遍历整个图所需的最小节点数可以使用深度优先搜索（DFS）或广度优先搜索（BFS）算法来实现。下面分别给出两种算法的代码示例：

1. 使用深度优先搜索（DFS）算法：

def dfs(graph, start, visited):
    visited.add(start)
    for neighbor in graph[start]:
        if neighbor not in visited:
            dfs(graph, neighbor, visited)
    return visited

def traverse_graph(graph):
    visited = set()
    count = 0
    for node in graph:
        if node not in visited:
            count += 1
            visited.update(dfs(graph, node, visited))
    return count

2. 使用广度优先搜索（BFS）算法：

from collections import deque

def bfs(graph, start, visited):
    queue = deque([start])
    visited.add(start)
    while queue:
        node = queue.popleft()
        for neighbor in graph[node]:
            if neighbor not in visited:
                visited.add(neighbor)
                queue.append(neighbor)
    return visited

def traverse_graph(graph):
    visited = set()
    count = 0
    for node in graph:
        if node not in visited:
            count += 1
            visited.update(bfs(graph, node, visited))
    return count

这两种算法的时间复杂度都为O(V + E)，其中V是节点数，E是边数。使用这些代码示例，可以计算出遍历整个图所需的最小节点数。