What is the difference between BFS and DFS?

BFS explores a graph level by level using a queue and finds the shortest path in unweighted graphs. DFS explores as deep as possible along each branch using a stack or recursion, useful for cycle detection and topological sorting.

When should I use Dijkstra vs A*?

Dijkstra finds the shortest path from a source to all nodes using edge weights. A* adds a heuristic that estimates distance to a specific goal, so it usually reaches a single target faster — but only Dijkstra works when no good heuristic exists.

What is a minimum spanning tree?

A minimum spanning tree (MST) connects all nodes of a weighted graph with the lowest total edge weight and no cycles. Prim's and Kruskal's algorithms both build MSTs — Prim grows from one node, Kruskal adds the cheapest valid edges.

Does Dijkstra work with negative edge weights?

No. Dijkstra assumes that once a node is finalized its shortest distance cannot improve, which breaks with negative weights. Use the Bellman-Ford algorithm for graphs containing negative edges.

All Tools›Algorithm›

Graph Algorithm Visualizer

Free · No Server

Visualize BFS, DFS, Dijkstra, A*, Prim, Kruskal, and Topological Sort on an interactive draggable graph with synced code.

Graph Algorithm Visualizer

Nodes7

Speed:

Undirected · drag nodes to reposition

Step 0 / 22

Initial graph.

Unvisited Frontier Current Visited / Tree Path

Pseudocode — live

queue = [start]; visited = {start}

while queue not empty:

u = queue.dequeue()

for each neighbor v of u:

if v not visited:

visited.add(v); queue.enqueue(v)

from collections import deque
def bfs(adj, start):
    vis = {start}
    q = deque([start])
    while q:
        u = q.popleft()
        for v in adj[u]:
            if v not in vis:
                vis.add(v); q.append(v)

About Breadth-First Search (BFS)

BFS explores a graph level by level using a queue, visiting all neighbors of a node before moving deeper. It finds the shortest path in unweighted graphs.

Time	O(V + E)
Space	O(V)
Graph	Undirected, unweighted

Real-world uses

Shortest path in unweighted graphs
Finding connected components
Web crawling and social-network levels
Grid/maze shortest path

What is Graph Visualizer?

The Graph Algorithm Visualizer on AiTechWorlds animates graph traversal and pathfinding algorithms on an interactive, draggable graph. Graph algorithms power maps, networks, dependency resolution, and social graphs, yet they are among the hardest topics to grasp from pseudocode alone. This tool animates breadth-first search (BFS), depth-first search (DFS), Dijkstra's shortest path, A* pathfinding, Prim's and Kruskal's minimum spanning tree, and topological sort — highlighting visited nodes, frontier queues, and chosen edges as each algorithm runs. Build your own graph by dragging nodes and adding edges, then watch the algorithm explore it with synced code. Everything runs in your browser — AiTechWorlds built this for CS students, interview candidates, and educators.

How to Use

1
Build or load a graph
Drag to position nodes and connect edges, or use a preset graph.
2
Select an algorithm
Choose BFS, DFS, Dijkstra, A*, Prim, Kruskal, or topological sort.
3
Pick start (and goal)
Set the start node, plus a goal node for pathfinding algorithms.
4
Run the visualization
Press Run to animate, or step through node-by-node.
5
Follow the highlights
Watch visited nodes, the frontier, and selected edges update live alongside the code.

Common Use Cases

CS students learning graph traversal and shortest-path algorithms
Interview candidates preparing for graph questions
Educators demonstrating BFS, DFS, and Dijkstra in class
Developers reasoning about pathfinding and MST trade-offs
Self-learners visualizing how A* uses heuristics

Key Features

Animates BFS, DFS, Dijkstra, A*, Prim, Kruskal, and topological sort
Interactive draggable graph builder
Highlights visited nodes, frontier, and chosen edges
Synced multi-step code view
Adjustable animation speed and step mode
Weighted and unweighted graph support
100% browser-based

🔒

100% Private — No Server Required

All processing happens directly in your browser. No data is uploaded, stored, or transmitted to any server.

Frequently Asked Questions

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Dynamic Programming Visualizer Backtracking Visualizer Searching Visualizer Sorting Visualizer

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Last reviewed on June 18, 2026 by the AiTechWorlds Tools Team. All processing runs locally in your browser.

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AiTechWorlds

from collections import deque def bfs(adj, start): vis = {start} q = deque([start]) while q: u = q.popleft() for v in adj[u]: if v not in vis: vis.add(v); q.append(v)

What is Graph Visualizer?

How to Use

Build or load a graph

Drag to position nodes and connect edges, or use a preset graph.

Select an algorithm

Choose BFS, DFS, Dijkstra, A*, Prim, Kruskal, or topological sort.

Pick start (and goal)

Set the start node, plus a goal node for pathfinding algorithms.

Run the visualization

Press Run to animate, or step through node-by-node.

Follow the highlights

Watch visited nodes, the frontier, and selected edges update live alongside the code.