# 深度优先搜索 def dfs(g, s, v): # 递归结束条件 if s in v: return v v.append(s) for i in range(len(g)): if i not in v and g[s][i]: v = dfs(g, i, v) return v # 广度优先搜索 def bfs(g, s): v = [] q = [] q.append(s) while q: s = q[0] q.remove(s) for i in range(len(g)): if i not in v and i not in q and g[s][i]: q.append(i) v.append(s) return v # 手动输入点和边数据 # # 定义输入匿名函数 # def f(): return map(int, input().split()) # # 输入点数和边数 # n, m = f() # # 初始化二维列表作为图邻阶矩阵 # g = [[0] * n for i in range(n)] # # 输入边集合 # for k in range(m): # i, j = f() # g[i][j] = 1 # g[j][i] = 1 # 手动输入的测试用例： # 8 10 # 0 1 # 0 4 # 1 5 # 2 3 # 2 5 # 2 6 # 3 6 # 3 7 # 5 6 # 6 7 # 手动输入后g的数据 g = [[0, 1, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 1, 1, 0], [0, 0, 1, 0, 0, 0, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 1, 0], [0, 0, 1, 1, 0, 1, 0, 1], [0, 0, 0, 1, 0, 0, 1, 0]] print(dfs(g, 1, [])) print(bfs(g, 1)) 文章来源: https://blog.csdn.net/daorenfeixueyuhau/article/details/89785163