getting_started

Getting Started with Py3plex

This directory contains introductory examples to help you get started with py3plex. These examples demonstrate the core concepts and basic usage patterns.

Quick Start Guide

New to py3plex? Follow this path:

1. 5-Minute Introduction: dsl_patterns_quick_reference.py

   • 7 essential DSL patterns (executable, copy-paste ready)
   • Covers 80% of typical use cases
   • Start here for fastest onboarding

2. 10-Minute Tutorial: tutorial_10min.py

   • Complete workflow from data loading to visualization
   • Understand multilayer network basics

3. Interactive Learning: example_ergonomics_demo.py

   • Use .hint() for context-aware suggestions
   • Learn DSL interactively as you code
   • Pedagogical error messages guide you

4. Deep Dive: See AGENTS.md for comprehensive documentation

Essential Patterns (from dsl_patterns_quick_reference.py)

The dsl_patterns_quick_reference.py file contains 7 copy-paste patterns for immediate use:

Pattern	Use Case	When to Use
1. Basic Filtering	Find high-degree nodes	Start here for most analyses
2. Cross-Layer Hubs	Nodes appearing in multiple layers	Multilayer structure insights
3. Uncertainty Quantification	Confidence intervals for metrics	Research publications
4. Layer Algebra	Complex layer selection	Advanced multilayer queries
5. Custom Metrics	Derive new metrics from existing	Feature engineering
6. Per-Layer Aggregation	Compare layers statistically	Layer-level analysis
7. Export Formats	Save results in various formats	Data integration

Pro Tip: Run dsl_patterns_quick_reference.py to see all patterns with live output!

Examples in This Category

Tutorials

• tutorial_10min.py - 10-minute tutorial covering network creation, analysis, and visualization

Using Built-in Datasets

• example_datasets.py - Load bundled datasets and generate synthetic networks (similar to scikit-learn's datasets module)

Creating Networks

• example_random_generator.py - Generate random multilayer Erdos-Renyi networks
• example_random_generators_advanced.py - Advanced random network generators with custom parameters
• example_multilayer_functionality.py - Core multilayer network operations (adding nodes, edges, layers)

Using NetworkX Integration

• example_networkx_wrapper.py - Apply NetworkX algorithms to multilayer networks
• example_networkx_wrapper_kwargs.py - NetworkX wrapper with keyword arguments support
• example_nx_wrapper.py - Compute betweenness centrality using NetworkX

What's Next?

After completing these examples, explore:

• I/O and Data - Learn to load and save networks in various formats
• Network Analysis - Analyze network properties and compute metrics
• Visualization - Create beautiful visualizations of your networks

Runtime Information

All examples in this directory are FAST (< 5 seconds) and standalone - perfect for learning!