StellarGraph vs NetworkX

Side-by-side — pricing, strengths, tradeoffs, and fit.

StellarGraph

Machine Learning on Graphs for network-structured data.

NetworkX

High-productivity software for complex network analysis and modeling.

At a glance

Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
· Not enough data·From $0

Overview

One-liner

Machine Learning on Graphs for network-structured data.

One-liner

High-productivity software for complex network analysis and modeling.

Maturity

Declining

Maturity

Not enough data

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Researchers and practitioners working with graph-structured data who need advanced machine learning capabilities.
Projects requiring integration of TensorFlow or Keras for deep learning on graphs.

✓ Best for

Researchers studying complex network structures who need a comprehensive set of algorithms for analysis.
Developers building applications that require graph manipulation and visualization capabilities.
Academics teaching or conducting research in the field of network science.

Not ideal for

✕ Not for

Developers looking for a cloud-based service without the need to manage local installations.
Teams that require real-time processing and cannot afford the latency associated with local computation.

✕ Not for

Projects requiring real-time processing of large-scale graphs due to performance limitations.
Applications needing a web-based interface for direct user interaction with graph data.

Strengths

↑ Strengths

Graph neural networks for node classification and link prediction
Supports various graph types including homogeneous, heterogeneous, and dynamic graphs
Integration with TensorFlow and Keras for deep learning on graphs

↑ Strengths

Support for directed, undirected, weighted, and unweighted graphs.
Algorithms for network analysis including centrality measures, clustering, and pathfinding.
Graph visualization capabilities using Matplotlib or other Python plotting libraries.
Extensive documentation and community support.

Weaknesses

↓ Weaknesses

Limited documentation for advanced use cases
Performance issues with very large graphs

↓ Weaknesses

Performance limitations with large graphs
Limited built-in visualization capabilities
Complex setup for advanced features
Limited support for distributed computing
Full StellarGraph profile →Full NetworkX profile →