👀 Views: 54 💬 Answers: 1 📅 Created: 2025-06-05

python networkx algorithms Python

I'm learning this framework and I'm writing unit tests and I'm working on a project and hit a roadblock....... I'm currently implementing Dijkstra's algorithm using the NetworkX library in Python, and I'm experiencing what seems to be a memory leak. The program runs fine for a small graph of around 100 nodes, but when I scale up to a graph with 10,000 nodes, the memory usage skyrockets, and eventually, the process is terminated due to excessive memory consumption. Here's a simplified version of my code: ```python import networkx as nx import time # Create a large random graph G = nx.gnp_random_graph(10000, 0.1) # Adding weights to the edges for (u, v) in G.edges(): G[u][v]['weight'] = 1 start_node = 0 end_node = 9999 # Function to run Dijkstra's algorithm start_time = time.time() shortest_path = nx.dijkstra_path(G, start_node, end_node) end_time = time.time() print(f"Shortest path from {start_node} to {end_node}: {shortest_path}") print(f"Execution time: {end_time - start_time} seconds") ``` I have tried optimizing the graph creation by reducing the edge probability, but it doesn't seem to help with memory spikes. Additionally, I've made sure to delete unused variables, but that doesn't seem to affect the overall memory footprint. When I run `memory_profiler`, it shows that a significant amount of memory is retained after the Dijkstra call, and I see a consistent increase in memory usage with larger graphs. Is there something I'm missing in terms of resource management or best practices for handling large graphs in NetworkX? Any advice on how to prevent this memory leak would be greatly appreciated! I'm working on a CLI tool that needs to handle this. What am I doing wrong? I'm coming from a different tech stack and learning Python. Thanks in advance! Any advice would be much appreciated.