Command-Line Interface (CLI) Tutorial

Py3plex includes a comprehensive command-line interface that provides access to all main algorithms and functionality directly from the terminal. This is useful for quick analysis, scripting, and automation workflows.

Installation

The CLI tool is automatically available after installing py3plex:

pip install git+https://github.com/SkBlaz/py3plex.git

After installation, verify the CLI is available:

py3plex --help

Alternative: Using Docker

If you prefer Docker, the CLI is also available via the Docker container:

# Build the Docker image
docker build -t py3plex:latest .

# Run CLI commands
docker run --rm py3plex:latest --help
docker run --rm -v $(pwd)/data:/data py3plex:latest create --nodes 100 --output /data/network.edgelist

See Docker Usage Guide for complete Docker documentation.

Quick Start

New to py3plex? Start with these essential commands:

# Run an interactive demo with example graph
py3plex quickstart

# Verify installation
py3plex selftest

# Check version
py3plex --version

# Get help
py3plex --help

The quickstart command creates a small demo multilayer network, computes statistics, generates a visualization, and shows you next steps. It’s the fastest way to see py3plex in action!

Getting Help

The CLI includes built-in documentation for all commands:

# Main help with all commands
py3plex --help

# Help for specific command
py3plex create --help
py3plex community --help
py3plex centrality --help

Available Commands

The py3plex CLI provides these main commands:

quickstart - Interactive demo with example graph (recommended for new users)
selftest - Verify installation and core functionality
check - Lint and validate graph data files
create - Create new multilayer networks
load - Load and inspect networks
community - Detect communities
centrality - Compute centrality measures
stats - Compute multilayer network statistics
visualize - Create network visualizations
aggregate - Aggregate multilayer networks into single layer
convert - Convert between network formats
help - Show detailed help information

Quick Reference

Essential flags:

--version - Display py3plex version
--help - Display help for any command

Common workflows:

# Quick demo
py3plex quickstart

# Validate → Load → Analyze
py3plex check data.csv
py3plex load data.csv --stats

# Create → Analyze → Visualize
py3plex create --nodes 50 --layers 2 --output net.graphml
py3plex load net.graphml --stats
py3plex visualize net.graphml --output viz.png

# Community detection workflow
py3plex community net.graphml --algorithm louvain --output communities.json

Validating Data Files

Before loading network data, you can validate file format and data integrity using the check command. This helps catch common issues early and provides actionable suggestions for fixing problems.

Basic Validation

Validate a graph data file (auto-detects format):

py3plex check network.csv

Output for valid file:

Checking file: network.csv
✓ No issues found!

Output for file with issues:

Checking file: network.csv

Found 5 issue(s):

[INFO] No layer columns found - this appears to be a single-layer network
[ERROR] Line 3: Empty destination node
  → Suggestion: Provide a valid destination node ID
[WARNING] Line 4: Negative weight: -1.5
  → Suggestion: Negative weights may not be supported by all algorithms
[WARNING] Line 5: Self-loop detected: Dave -> Dave
  → Suggestion: Self-loops may not be supported by all algorithms
[WARNING] Line 6: Duplicate edge: Alice -> Bob
  → Suggestion: Remove duplicate edges or consolidate with edge weights

Linting results:
  Errors: 1
  Warnings: 3
  Info: 1

✗ Validation failed with errors

Supported File Formats

The check command automatically detects and validates these formats:

CSV - Comma-separated values with headers (e.g., network.csv)
Edgelist - Space-separated node pairs (e.g., network.edgelist)
Multiedgelist - Space-separated with layer information (e.g., network.txt)

Strict Mode

Use --strict to treat warnings as errors (useful for CI/CD pipelines):

py3plex check network.csv --strict

With strict mode enabled, the command exits with error code 1 if any warnings are found, making it suitable for automated validation in scripts.

Validation Checks

The linter performs these checks:

Errors (cause validation failure):

Missing or empty node IDs
Invalid weight values (non-numeric)
Missing required columns (for CSV files)
Malformed file structure

Warnings (reported but don’t fail validation):

Negative weights
Self-loops
Duplicate edges

Info (informational messages):

Single-layer network detected (when no layer columns found)
Format detection results

Example Workflows

Validate before loading:

# Step 1: Validate the file
py3plex check my_network.csv

# Step 2: If valid, load and analyze
py3plex load my_network.csv --stats

CI/CD integration:

# In your CI script - fail if any issues found
py3plex check network.csv --strict || exit 1

# Proceed with analysis if validation passed
py3plex load network.csv --stats

Batch validation:

# Validate multiple files
for file in data/*.csv; do
    echo "Checking $file..."
    py3plex check "$file"
done

Creating Networks

Create Random Networks

Create a simple multilayer network with random edges:

py3plex create --nodes 100 --layers 3 --output network.graphml --seed 42

This creates a network with 100 nodes per layer and 3 layers, saving it to network.graphml.

Network Generation Models

Erdős-Rényi Random Graphs:

py3plex create --nodes 50 --layers 2 --type er --probability 0.15 \
    --output er_network.graphml --seed 42

Barabási-Albert Preferential Attachment:

py3plex create --nodes 100 --layers 3 --type ba --probability 0.05 \
    --output ba_network.graphml --seed 42

Watts-Strogatz Small-World:

py3plex create --nodes 50 --layers 2 --type ws --probability 0.2 \
    --output ws_network.graphml --seed 42

Output Formats

The CLI supports multiple output formats (automatically detected from file extension):

.graphml - GraphML format (XML-based, recommended)
.gexf - GEXF format (XML-based, for Gephi)
.gpickle - Python pickle format (binary, fastest)

Loading and Inspecting Networks

Basic Network Information

Display basic network statistics:

py3plex load network.graphml --info

Output:

Network Information:
  Nodes: 300
  Edges: 450
  Layers: 3 (layer1, layer2, layer3)
  Directed: False

Detailed Statistics

Get comprehensive statistics including layer densities and clustering:

py3plex load network.graphml --stats

Output:

Network Information:
  Nodes: 300
  Edges: 450
  Layers: 3 (layer1, layer2, layer3)
  Directed: False

Basic Statistics:
  Layer Densities:
    layer1: 0.0303
    layer2: 0.0298
    layer3: 0.0305
  Avg Clustering: 0.0421
  Avg Degree: 3.00
  Max Degree: 12

Save to JSON

Export network information to JSON for further processing:

py3plex load network.graphml --info --output network_info.json

Community Detection

Louvain Algorithm

Detect communities using the Louvain modularity optimization algorithm:

py3plex community network.graphml --algorithm louvain --output communities.json

Output:

Loading network from network.graphml...
Detecting communities using louvain...
Found 8 communities
Community sizes: min=15, max=52, avg=37.5
Communities saved to communities.json

With Custom Resolution

Adjust the resolution parameter to find more or fewer communities:

# Higher resolution = more communities
py3plex community network.graphml --algorithm louvain --resolution 1.5

# Lower resolution = fewer communities
py3plex community network.graphml --algorithm louvain --resolution 0.5

Label Propagation

Use label propagation for fast community detection:

py3plex community network.graphml --algorithm label_prop --output communities.json

Infomap (Optional)

If Infomap is installed, use it for overlapping community detection:

py3plex community network.graphml --algorithm infomap --output communities.json

Centrality Measures

Degree Centrality

Compute degree centrality and show top nodes:

py3plex centrality network.graphml --measure degree --top 10

Output:

Loading network from network.graphml...
Computing degree centrality...

Top 10 nodes by degree centrality:
  node42---layer1: 12.000000
  node18---layer2: 11.000000
  node77---layer1: 10.000000
  ...

Save Results

Export centrality scores to JSON:

py3plex centrality network.graphml --measure degree --output centrality.json

All Centrality Measures

Betweenness Centrality:

py3plex centrality network.graphml --measure betweenness --top 10

Closeness Centrality:

py3plex centrality network.graphml --measure closeness --top 10

Eigenvector Centrality:

py3plex centrality network.graphml --measure eigenvector --top 10

PageRank:

py3plex centrality network.graphml --measure pagerank --top 10

Multilayer Network Statistics

Comprehensive Statistics

Compute all available multilayer statistics:

py3plex stats network.graphml --measure all --output stats.json

Output includes:

Layer densities for each layer
Clustering coefficient
Node activity samples
Versatility centrality (top nodes)
Edge overlap between layers

Specific Statistics

Layer Density:

py3plex stats network.graphml --measure layer_density

Clustering Coefficient:

py3plex stats network.graphml --measure clustering

Node Activity:

py3plex stats network.graphml --measure node_activity

Versatility Centrality:

py3plex stats network.graphml --measure versatility

Edge Overlap:

py3plex stats network.graphml --measure edge_overlap

Network Visualization

Multilayer Layout

Create a visualization using the specialized multilayer layout:

py3plex visualize network.graphml --layout multilayer --output network.png

Standard Layouts

Use NetworkX layouts for different visual styles:

Spring Layout (Force-Directed):

py3plex visualize network.graphml --layout spring --output network_spring.png

Circular Layout:

py3plex visualize network.graphml --layout circular --output network_circular.png

Kamada-Kawai Layout:

py3plex visualize network.graphml --layout kamada_kawai --output network_kk.png

Custom Figure Size

Adjust the output image dimensions:

py3plex visualize network.graphml --layout spring --width 15 --height 10 \
    --output large_viz.png

Network Aggregation

Aggregate multiple layers into a single layer using different methods:

Sum Aggregation

Sum edge weights across layers:

py3plex aggregate network.graphml --method sum --output aggregated.graphml

Mean Aggregation

Average edge weights across layers:

py3plex aggregate network.graphml --method mean --output aggregated_mean.graphml

Other Aggregation Methods

max - Take maximum edge weight across layers
min - Take minimum edge weight across layers

Format Conversion

Convert Between Formats

Convert networks between different file formats:

To GEXF (for Gephi):

py3plex convert network.graphml --output network.gexf

To JSON:

py3plex convert network.graphml --output network.json

To gpickle (Python binary):

py3plex convert network.graphml --output network.gpickle

Complete Workflow Examples

Example 1: Create, Analyze, and Visualize

# Step 1: Create a Barabási-Albert network
py3plex create --nodes 100 --layers 3 --type ba --probability 0.05 \
    --output ba_network.graphml --seed 42

# Step 2: Detect communities
py3plex community ba_network.graphml --algorithm louvain \
    --output communities.json

# Step 3: Compute centrality
py3plex centrality ba_network.graphml --measure pagerank --top 20 \
    --output central_nodes.json

# Step 4: Visualize
py3plex visualize ba_network.graphml --layout spring --width 15 --height 10 \
    --output ba_visualization.png

Example 2: Load, Analyze Statistics, and Export

# Step 1: Load and inspect
py3plex load existing_network.graphml --stats

# Step 2: Compute comprehensive statistics
py3plex stats existing_network.graphml --measure all --output stats.json

# Step 3: Convert to GEXF for Gephi
py3plex convert existing_network.graphml --output for_gephi.gexf

Example 3: Multiple Analysis Pipeline

# Create network
py3plex create --nodes 50 --layers 2 --type er --probability 0.15 \
    --output network.graphml --seed 42

# Run all centrality measures
py3plex centrality network.graphml --measure degree --output degree.json
py3plex centrality network.graphml --measure betweenness --output betweenness.json
py3plex centrality network.graphml --measure pagerank --output pagerank.json

# Detect communities with different algorithms
py3plex community network.graphml --algorithm louvain --output louvain.json
py3plex community network.graphml --algorithm label_prop --output label_prop.json

# Create multiple visualizations
py3plex visualize network.graphml --layout multilayer --output viz_ml.png
py3plex visualize network.graphml --layout spring --output viz_spring.png
py3plex visualize network.graphml --layout circular --output viz_circular.png

Example 4: Validate External Data Before Analysis

# Step 1: Validate the data file
py3plex check external_data.csv

# Step 2: If validation passes, load the network
py3plex load external_data.csv --info

# Step 3: Run analysis
py3plex stats external_data.csv --measure all --output stats.json

# Step 4: Detect communities
py3plex community external_data.csv --algorithm louvain --output communities.json

# Step 5: Create visualization
py3plex visualize external_data.csv --layout spring --output network_viz.png

Tips and Best Practices

Validate Data Files First: Always run py3plex check on external data files before analysis to catch format issues early.
Use Seeds for Reproducibility: Always use --seed when creating networks for reproducible results.
JSON Output for Scripting: Use --output to save results as JSON for post-processing with other tools.
Check Help for Each Command: Each command has detailed help with examples:
```
py3plex <command> --help
```

Format Auto-Detection: The CLI automatically detects file formats from extensions, so use appropriate extensions (.graphml, .gexf, .json, .gpickle).
Large Networks: For large networks (>1000 nodes), prefer:
- spring or circular layouts for visualization
- louvain for community detection (faster than infomap)
- degree centrality for quick analysis
Batch Processing: Combine CLI commands with shell scripts for batch processing multiple networks.

Common Issues and Solutions

Issue: “Infomap not available”

Solution: Infomap requires separate installation. Use louvain or label_prop instead.

Issue: “Eigenvector centrality failed”

Solution: Some networks don’t converge for eigenvector centrality. The CLI automatically falls back to degree centrality.

Issue: Visualization too slow

Solution: Use simpler layouts (circular) or reduce figure size (--width 8 --height 6).

Issue: Memory errors with large networks

Solution: Use aggregation to reduce network size before visualization, or compute statistics without visualization.

Command-Line Interface (CLI) Tutorial

Installation

Quick Start

Getting Help

Available Commands

Quick Reference

Validating Data Files

Basic Validation

Supported File Formats

Strict Mode

Validation Checks

Example Workflows

Creating Networks

Create Random Networks

Network Generation Models

Output Formats

Loading and Inspecting Networks

Basic Network Information

Detailed Statistics

Save to JSON

Community Detection

Louvain Algorithm

With Custom Resolution

Label Propagation

Infomap (Optional)

Centrality Measures

Degree Centrality

Save Results

All Centrality Measures

Multilayer Network Statistics

Comprehensive Statistics

Specific Statistics

Network Visualization

Multilayer Layout

Standard Layouts

Custom Figure Size

Network Aggregation

Sum Aggregation

Mean Aggregation

Other Aggregation Methods

Format Conversion

Convert Between Formats

Complete Workflow Examples

Example 1: Create, Analyze, and Visualize

Example 2: Load, Analyze Statistics, and Export

Example 3: Multiple Analysis Pipeline

Example 4: Validate External Data Before Analysis

Tips and Best Practices

Common Issues and Solutions

See Also