When working with graphs in Python, two of the most popular libraries are igraph and networkx. Both offer a range of features for network analysis, including shortest path algorithms like Dijkstra's. But which one should you choose? This post walks through a hands-on comparison using Dijkstra’s algorithm, with insights on performance, ease of use, and real-world suitability.
Libraries Overview
networkx is a pure Python library known for its simplicity and ease of use. It’s ideal for small to medium-sized graphs and educational purposes.
igraph is written in C and optimized for performance. It’s great for large-scale networks where speed and memory efficiency matter.
Let’s compare how both libraries handle Dijkstra’s algorithm.
Installation
First, install both libraries:
pip install networkx
pip install igraph
If you're using igraph for the first time, make sure you get the Python bindings, not just the C core.
pip install python-igraph
Example: Finding Shortest Path with Dijkstra
Let’s define a simple directed graph with weighted edges and compute the shortest path from node A to node D.
Using networkx
import networkx as nx
G = nx.DiGraph()
G.add_weighted_edges_from([
('A', 'B', 1),
('B', 'C', 2),
('A', 'C', 4),
('C', 'D', 1)
])
path = nx.dijkstra_path(G, source='A', target='D')
length = nx.dijkstra_path_length(G, source='A', target='D')
print("Shortest path:", path)
print("Total distance:", length)
Output:
Shortest path: ['A', 'B', 'C', 'D']
Total distance: 4
Using igraph
from igraph import Graph
edges = [('A', 'B'), ('B', 'C'), ('A', 'C'), ('C', 'D')]
weights = [1, 2, 4, 1]
# Convert node names to indices
vertices = ['A', 'B', 'C', 'D']
vertex_map = {name: idx for idx, name in enumerate(vertices)}
g = Graph(directed=True)
g.add_vertices(vertices)
g.add_edges([(vertex_map[u], vertex_map[v]) for u, v in edges])
g.es['weight'] = weights
shortest = g.get_shortest_paths(vertex_map['A'], to=vertex_map['D'], weights='weight', output='vpath')
distance = g.shortest_paths(vertex_map['A'], to=vertex_map['D'], weights='weight')[0][0]
# Convert indices back to names
path = [vertices[i] for i in shortest[0]]
print("Shortest path:", path)
print("Total distance:", distance)
Output:
Shortest path: ['A', 'B', 'C', 'D']
Total distance: 4.0
Comparison: networkx vs igraph
| Feature | networkx | igraph |
|---|---|---|
| Language | Pure Python | C core with Python bindings |
| Speed | Slower on large graphs | Much faster on large graphs |
| Memory Usage | Higher | Lower |
| Ease of Use | Very beginner-friendly | Slightly steeper learning curve |
| Best Use Case | Small to medium graphs, prototyping | Large-scale graphs, performance-critical tasks |
| Shortest Path Output | Node names | Node indices (manual mapping needed) |
When to Use Which?
Use
networkxif you're working on academic projects, prototyping, or working with small to mid-sized graphs where clarity and flexibility matter more than speed.Use
igraphif you're dealing with large networks (e.g., social graphs, biological networks), and you need high performance and low memory usage.
Final Thoughts
Both libraries implement Dijkstra's algorithm effectively, but your choice should depend on the size of your graph and your performance needs. For quick scripts and readable code, go with networkx. If you're pushing the limits of graph size or speed, igraph is the better option.
Being familiar with both gives you the flexibility to switch depending on your project needs. Try both, benchmark with your own data, and choose what feels right for your workflow.
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