Dijkstra's Shortest Path Calculator

Q: What is Dijkstra's algorithm?

Dijkstra's algorithm is a graph search algorithm that finds the shortest path from a source node to all other nodes in a weighted graph with non-negative edge weights. It uses a greedy strategy with a priority queue (min-heap), always processing the unvisited node with the smallest known distance. The time complexity is O((V + E) log V) with a binary heap.

Q: Can Dijkstra's algorithm handle negative edge weights?

No, Dijkstra's algorithm requires all edge weights to be non-negative. Negative weights can cause the algorithm to produce incorrect results because once a node is marked as visited, its distance is considered final. For graphs with negative weights (but no negative cycles), use the Bellman-Ford algorithm instead.

Q: What is the difference between directed and undirected graphs?

In a directed graph, edges have a direction — an edge from A to B does not imply an edge from B to A. In an undirected graph, edges work both ways — if there is a connection between A and B, you can travel in either direction. Road networks are typically modeled as undirected graphs, while web links and flight routes are directed.

Q: What is edge relaxation in Dijkstra's algorithm?

Edge relaxation is the core operation in Dijkstra's algorithm. When visiting a node u, for each neighbor v, the algorithm checks if the path through u (dist[u] + weight(u,v)) is shorter than the current known distance to v (dist[v]). If it is shorter, the distance is updated (relaxed) and v is added to the priority queue with the new distance.

Q: What is a shortest path tree?

A shortest path tree (SPT) is a subgraph rooted at the source node that contains the shortest paths from the source to all reachable nodes. Each node (except the source) has exactly one parent — the predecessor on its shortest path. The SPT is a byproduct of Dijkstra's algorithm and is useful for routing and network design.

Q: What are real-world applications of Dijkstra's algorithm?

Dijkstra's algorithm is used in GPS navigation systems, network routing protocols (OSPF, IS-IS), social network analysis, airline route optimization, robot path planning, game AI pathfinding, supply chain logistics, and telecommunications network design. Any problem that can be modeled as finding the shortest or least-cost path in a graph benefits from this algorithm.

Enter a weighted graph and the start and end to get the shortest path, a priority-queue trace and the path tree. Limited to 50 nodes and 200 edges.

3 free uses dailyNo sign-up requiredUpdated Feb 2026

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About Dijkstra's Shortest Path Calculator

Welcome to the Dijkstra's Shortest Path Calculator, an interactive tool for finding shortest paths in weighted graphs using Dijkstra's algorithm. Whether you are a computer science student learning graph theory, a networking professional analyzing routing protocols, or a developer implementing pathfinding, this calculator provides a visual, step-by-step exploration of one of the most fundamental algorithms in computer science.

What is Dijkstra's Algorithm?

Dijkstra's algorithm, invented by Dutch computer scientist Edsger W. Dijkstra in 1956, is a graph search algorithm that solves the single-source shortest path problem for a weighted graph with non-negative edge weights. Given a source node, it finds the shortest path from that node to every other node in the graph.

The algorithm works by maintaining a set of unvisited nodes and repeatedly selecting the unvisited node with the smallest tentative distance (a greedy strategy). This is what makes it both elegant and efficient — once a node is "visited," its shortest distance is guaranteed to be final.

Algorithm Pseudocode

function Dijkstra(Graph, source):
  for each node v in Graph:
    dist[v] ← ∞
    prev[v] ← undefined
  dist[source] ← 0
  Q ← priority queue of all nodes

  while Q is not empty:
    u ← node in Q with minimum dist[u]
    remove u from Q

    for each neighbor v of u still in Q:
      alt ← dist[u] + weight(u, v)
      if alt < dist[v]:
        dist[v] ← alt // relaxation
        prev[v] ← u

  return dist[], prev[]

Core Formula

Edge Relaxation Condition

$$\text{if } d[u] + w(u, v) < d[v] \text{ then } d[v] \leftarrow d[u] + w(u, v)$$

Where:

d[u] = current shortest known distance from source to node u
w(u, v) = weight of the edge from u to v
d[v] = current shortest known distance from source to node v

How to Use This Calculator

Define your graph edges: Enter edges in the format source, destination, weight, one per line. Each line represents a connection between two nodes.
Choose graph type: Select "Undirected" for two-way edges (like roads) or "Directed" for one-way edges (like flight routes or web links).
Set the source node: Enter the starting node from which all shortest paths will be calculated.
Find shortest paths: Click the button to run Dijkstra's algorithm. Explore the interactive graph visualization, distance table, and step-by-step trace.
Walk through steps: Use the Next/Previous buttons to see the algorithm execute one step at a time, with the graph highlighting updated nodes and edges in real time.

Understanding the Results

Distance Table

Shows the shortest distance from the source to every node, along with the complete path. Nodes marked ∞ are unreachable from the source.

Graph Visualization

The interactive canvas shows your graph with color-coded nodes and edges:

Blue node = Source node
Green nodes = Visited (finalized distance)
Orange node = Currently being processed
Gray nodes = Not yet visited
Green edges = Shortest path tree

Step-by-Step Trace

Shows the algorithm's execution including priority queue extractions, edge relaxations, and distance updates at each step. This is invaluable for learning how the algorithm works.

Time Complexity

The efficiency of Dijkstra's algorithm depends on the data structure used for the priority queue:

Priority Queue	Time Complexity	Best For
Binary Heap	$ O((V + E) \log V) $	General-purpose, sparse graphs
Fibonacci Heap	$ O(V \log V + E) $	Dense graphs (theoretical)
Array (no heap)	$ O(V^2) $	Very dense graphs, small V

Where V is the number of vertices (nodes) and E is the number of edges. This calculator uses a binary heap implementation.

Directed vs. Undirected Graphs

Undirected graph: An edge between A and B means you can travel both A→B and B→A. Examples: road networks, friendship networks, electrical circuits.
Directed graph: An edge from A to B only allows travel A→B, not necessarily B→A. Examples: web hyperlinks, Twitter followers, flight routes, river flow.

Limitations of Dijkstra's Algorithm

No negative weights: Dijkstra's algorithm produces incorrect results with negative edge weights. Use Bellman-Ford for graphs with negative weights (but no negative cycles).
Single source: It finds shortest paths from one source. For all-pairs shortest paths, use Floyd-Warshall or run Dijkstra from each node.
No negative cycles: Negative cycles make shortest paths undefined (you can always go around the cycle to reduce distance infinitely).

Real-World Applications

GPS Navigation

Mapping services use variants of Dijkstra's algorithm (often with A* heuristics) to find the fastest route between two locations. Road intersections are nodes, and road segments are weighted edges (by time or distance).

Network Routing

Internet routing protocols like OSPF (Open Shortest Path First) and IS-IS use Dijkstra's algorithm to compute shortest paths through network topologies. Each router builds a shortest path tree to determine packet forwarding.

Social Network Analysis

Finding the shortest connection path between users (degrees of separation), computing centrality measures, and identifying influential nodes in a network.

Game Development

NPC pathfinding in video games uses Dijkstra's algorithm or A* (which extends Dijkstra with heuristics) to navigate game maps and find optimal movement paths.

Supply Chain & Logistics

Optimizing delivery routes, warehouse-to-store paths, and multi-modal transportation networks where different transportation methods have different costs.

Frequently Asked Questions

What is Dijkstra's algorithm?

Dijkstra's algorithm is a graph search algorithm that finds the shortest path from a source node to all other nodes in a weighted graph with non-negative edge weights. It uses a greedy strategy with a priority queue (min-heap), always processing the unvisited node with the smallest known distance. The time complexity is O((V + E) log V) with a binary heap.

Can Dijkstra's algorithm handle negative edge weights?

No, Dijkstra's algorithm requires all edge weights to be non-negative. Negative weights can cause the algorithm to produce incorrect results because once a node is marked as visited, its distance is considered final. For graphs with negative weights (but no negative cycles), use the Bellman-Ford algorithm instead.

What is the difference between directed and undirected graphs?

In a directed graph, edges have a direction — an edge from A to B does not imply an edge from B to A. In an undirected graph, edges work both ways — if there is a connection between A and B, you can travel in either direction. Road networks are typically modeled as undirected graphs, while web links and flight routes are directed.

What is edge relaxation in Dijkstra's algorithm?

Edge relaxation is the core operation in Dijkstra's algorithm. When visiting a node u, for each neighbor v, the algorithm checks if the path through u (dist[u] + weight(u,v)) is shorter than the current known distance to v (dist[v]). If it is shorter, the distance is updated (relaxed) and v is added to the priority queue with the new distance.

What is a shortest path tree?

A shortest path tree (SPT) is a subgraph rooted at the source node that contains the shortest paths from the source to all reachable nodes. Each node (except the source) has exactly one parent — the predecessor on its shortest path. The SPT is a byproduct of Dijkstra's algorithm and is useful for routing and network design.

What are real-world applications of Dijkstra's algorithm?

Dijkstra's algorithm is used in GPS navigation systems, network routing protocols (OSPF, IS-IS), social network analysis, airline route optimization, robot path planning, game AI pathfinding, supply chain logistics, and telecommunications network design. Any problem that can be modeled as finding the shortest or least-cost path in a graph benefits from this algorithm.

Additional Resources

Reference this content, page, or tool as:

"Dijkstra's Shortest Path Calculator" at https://MiniWebtool.com/dijkstra-s-shortest-path-calculator/ from MiniWebtool, https://MiniWebtool.com/

by miniwebtool team. Updated: Feb 19, 2026

Developer API available: Run this tool from your app, automation, or agent with one JSON HTTP request. View API docs