👀 Views: 3 💬 Answers: 1 📅 Created: 2025-06-02

algorithm dijkstra performance graph Python

I keep running into Hey everyone, I'm running into an issue that's driving me crazy... Quick question that's been bugging me - After trying multiple solutions online, I still can't figure this out. I'm currently implementing Dijkstra's algorithm to find the shortest path in a large, sparse graph using Python. The graph is represented as an adjacency list, and I'm using a priority queue from the `heapq` library to efficiently retrieve the next node with the smallest distance. However, I've noticed that my implementation is performing poorly on graphs with millions of nodes and edges, specifically when it comes to processing time. Here’s the core of my implementation: ```python import heapq from collections import defaultdict def dijkstra(graph, start): distances = {vertex: float('infinity') for vertex in graph} distances[start] = 0 priority_queue = [(0, start)] while priority_queue: current_distance, current_vertex = heapq.heappop(priority_queue) if current_distance > distances[current_vertex]: continue for neighbor, weight in graph[current_vertex]: distance = current_distance + weight if distance < distances[neighbor]: distances[neighbor] = distance heapq.heappush(priority_queue, (distance, neighbor)) return distances ``` While this works fine for smaller graphs, I'm working with performance optimization with larger graphs, especially when the density increases. The algorithm takes significantly longer than expected, and I've received timeout errors on tests with larger datasets. I've tried optimizing the code by using a dictionary for the priority queue and pre-computing the neighbors, but it hasn’t helped much. I’ve also considered switching to a Fibonacci heap for better amortized time complexity, yet that seems overly complex for my use case. Is there a more efficient approach or perhaps a different algorithm that I should consider for handling large sparse graphs in Python? Any optimization tips would be greatly appreciated! What's the best practice here? My development environment is Windows. This is happening in both development and production on Windows 11. Any suggestions would be helpful.