Graph implementation

Step By Step Implementation Of Graph

Creating and working with a graph involves several steps. I’ll outline a step-by-step approach for creating and interacting with a basic undirected graph in Python.

Step 1: Define the Graph Class

class Graph:
    def __init__(self):
        self.graph = {}
    def add_node(self, node):
        if node not in self.graph:
            self.graph[node] = []
    def add_edge(self, node1, node2):
        self.add_node(node1)
        self.add_node(node2)
        self.graph[node1].append(node2)
        self.graph[node2].append(node1)
    def display(self):
        for node in self.graph:
            print(f"{node}: {', '.join(map(str, self.graph[node]))}")

Step 2: Create an Instance of the Graph

g = Graph()

Step 3: Add Nodes to the Graph

g.add_node(1)
g.add_node(2)
g.add_node(3)
g.add_node(4)

Step 4: Add Edges to the Graph

g.add_edge(1, 2)
g.add_edge(1, 3)
g.add_edge(2, 4)
g.add_edge(3, 4)

Step 5: Display the Graph

g.display()

The output should be:

1: 2, 3
2: 1, 4
3: 1, 4
4: 2, 3

Step 6: Implement Graph Traversal (Optional)

You can implement depth-first search (DFS) or breadth-first search (BFS) to traverse the graph:

def dfs(graph, start, visited=None):
    if visited is None:
        visited = set()
    print(start)
    visited.add(start)
    for neighbor in graph[start]:
        if neighbor not in visited:
            dfs(graph, neighbor, visited)
# Example DFS traversal
dfs(g.graph, 1)

Step 7: Extend Functionality (Optional)

Depending on your needs, you can extend the Graph class to handle directed graphs, weighted edges, shortest path algorithms, or other graph-related operations.