Py3plex: Multilayer Network Analysis

py3plex provides scalable analysis and visualization of multilayer and multiplex networks in Python.

Key features at a glance:

Multiplex and multilayer network structures
SQL-like DSL for network queries — first-class feature
17+ multilayer statistics and centrality measures
Community detection (Louvain, Infomap, multilayer modularity)
Random walk algorithms (Node2Vec, DeepWalk) for embeddings
Publication-ready visualizations
High-performance I/O with Arrow/Parquet support
Full NetworkX compatibility

DSL: Query Networks Like SQL

The py3plex DSL lets you query networks using intuitive SQL-like syntax or a type-safe Python builder API. Both return the same results; pick the style that fits your workflow.

from py3plex.dsl import execute_query, Q, L

# String DSL: Simple and readable
result = execute_query(network,
    'SELECT nodes WHERE layer="social" AND degree > 5 '
    'COMPUTE betweenness_centrality'
)

# Builder API: Type-safe with autocompletion
result = (
    Q.nodes()
     .from_layers(L["social"])
     .where(degree__gt=5)
     .compute("betweenness_centrality")
     .order_by("-betweenness_centrality")
     .limit(10)
     .execute(network)
)

The DSL is perfect for:

Interactive network exploration
Rapid prototyping
Educational purposes
Production pipelines

See the complete SQL-like DSL for Multilayer Networks guide for all features!

Quickstart

Get started in three steps: install, build a tiny multilayer network, and inspect it.

Step 1 — Install

pip install py3plex

Step 2 — Create a multilayer network

from py3plex.core import multinet

network = multinet.multi_layer_network()
network.add_edges([
    ['Alice', 'friends', 'Bob', 'friends', 1],
    ['Bob', 'friends', 'Carol', 'friends', 1],
    ['Alice', 'colleagues', 'Bob', 'colleagues', 1],
], input_type="list")

network.basic_stats()
network.visualize_network(show=True)

Expected output

Number of nodes: 6
Number of edges: 3

Counts reflect per-layer node identities (Alice/Bob/Carol each appear in two layers). Your totals will differ if you change the edge list. The visualization opens in a Matplotlib window and colors nodes by layer.

Start Here

New to py3plex? Start with 10-Minute Tutorial to move from basics to a full analysis in about 10 minutes.

Already familiar with multilayer networks? Jump to How to Run Community Detection on Multilayer Networks or browse How-to Guides for task-focused guides.

Just need the API? See API Documentation or DSL Reference.

Want to understand the concepts? Read Multilayer Networks 101 for the theory behind multilayer networks.

Documentation Structure

This documentation follows the Diátaxis framework with 7 top-level sections. Use the outline below to jump directly to the content type you need—tutorials for learning, how-to guides for tasks, explanations for concepts, and reference for precise details.

Part I: Overview

High-level introduction to py3plex and multilayer networks.

Part II: Getting Started (Tutorials)

Step-by-step tutorials for beginners. Start here if you’re new to py3plex.

Getting Started

Part III: How-to Guides

Task-oriented guides answering “How do I…?” questions.

How-to Guides

Part IV: Concepts & Explanations

Theoretical background and architectural explanations.

Concepts & Explanations

Part V: API & DSL Reference

Complete reference documentation for APIs and DSL syntax.

API & DSL Reference

Part VI: Examples & Recipes

Complete working examples and analysis recipes.

Examples & Recipes

Part VII: Project Info

Project information, contributing guidelines, and citations.

Project Info

Additional Sections

Deployment & GUI

Developer Docs

Citation

If you use py3plex in your research, please cite:

@Article{Skrlj2019,
  author={Skrlj, Blaz and Kralj, Jan and Lavrac, Nada},
  title={Py3plex toolkit for visualization and analysis of multilayer networks},
  journal={Applied Network Science},
  year={2019},
  volume={4},
  number={1},
  pages={94},
  doi={10.1007/s41109-019-0203-7}
}