This document contains code to perform depth-first search (DFS) and breadth-first search (BFS) on a graph. The DFS code recursively visits all nodes connected to the starting node by traversing depth-wise, printing each node. The BFS code uses a queue to iteratively visit nodes level-by-level starting from the starting node, printing nodes in the order they are visited. Both algorithms are demonstrated on a sample graph with nodes A through J.

1. DFS Search
Code:
graph = {
'A' : ['b','C'],
'B' : ['d', 'e'],
'C' : ['h'],
'D' : ['f'],
'E' : ['g'],
'G' : [],
'F' : [],
'H' : ['i'],
'I' : ['j'],
'J' : []
}
visited = set()
def dfs(visited, graph, node):
if node not in visited:
print (node)
visited.add(node)
for neighbour in graph[node]:
dfs(visited, graph, neighbour)
print("Following is the Depth-First Search")
dfs(visited, graph, 'A')

2. BFS Search:
Code:
graph = {
'A' : ['b','C'],
'B' : ['d', 'e'],
'C' : ['h'],
'D' : ['f'],
'E' : ['g'],
'G' : [],
'F' : [],
'H' : ['i'],
'I' : ['j'],
'J' : []
}
visited = []
queue = []
def bfs(visited, graph, node):
visited.append(node)
queue.append(node)
while queue:
m = queue.pop(0)
print (m, end = " ")
for neighbour in graph[m]:
if neighbour not in visited:
visited.append(neighbour)

3. queue.append(neighbour)
print("Following is the Breadth-First Search")
bfs(visited, graph, 'A')