lanet-vi 5.0.0

Large scale network visualization using k-core, k-dense, and d-core decomposition with community detection

LaNet-vi 5.0

Large-scale network visualization using k-core decomposition

LaNet-vi is a Python package for visualizing large-scale networks through hierarchical decomposition algorithms. It reveals network structure by identifying the k-core hierarchy - from peripheral nodes to densely connected cores.

What is K-Core Decomposition?

K-core decomposition identifies hierarchical layers in networks where each k-core is a maximal subgraph with all nodes having at least k neighbors. This creates an "onion-like" structure revealing:

• Core nodes (high k): Densely connected, central, resilient
• Peripheral nodes (low k): Loosely connected, on the edges
• Intermediate layers: Transitional connectivity

Perfect for analyzing social networks, internet topology, biological networks, and collaboration graphs.

📖 Learn more about k-core concepts →

Features

• K-core, k-dense, and d-core decomposition algorithms
• Circular hierarchical layout with smooth rings and gradient edge coloring
• High-performance rendering for networks with millions of nodes
• Flexible I/O supporting compressed formats (gzip, bz2)
• Community detection with Louvain and modularity algorithms
• Python API and CLI with full configurability
• Publication-ready visualizations with auto-scaling legends

Installation

# Using uv (recommended)
uv pip install lanet-vi

# Or with pip
pip install lanet-vi

Quick Start

Command Line

# Visualize a network
lanet-vi visualize --input network.txt --output viz.png

# With custom settings
lanet-vi visualize --input network.txt \
  --width 2400 --height 2400 \
  --background black \
  --output viz.png

# Generate configuration template
lanet-vi config my_config.yaml

Python API

import networkx as nx
from lanet_vi import Network, LaNetConfig, DecompositionType

# Load network
G = nx.karate_club_graph()

# Decompose and visualize
config = LaNetConfig()
net = Network(G, config)
net.decompose(DecompositionType.KCORES)
net.visualize("output.png")

Example: Internet Topology

from lanet_vi.io.readers import read_caida_snapshot
from lanet_vi import Network, LaNetConfig

# Download and visualize CAIDA AS-relationships data
graph, _ = read_caida_snapshot(
    "https://publicdata.caida.org/datasets/as-relationships/serial-1/20170101.as-rel.txt.bz2"
)

config = LaNetConfig()  # Uses optimized defaults
net = Network(graph, config)
net.decompose()
net.visualize("internet_topology.png")

See example output: examples/outputs/caida_as_relationships_kcores.png

The visualization reveals the Internet's hierarchical structure with Tier-1 providers in the center and stub networks at the periphery.

Example Visualizations

CAIDA AS-Relationships Network (56,345 nodes): K-cores (left) vs K-denses (right)

The visualizations reveal the hierarchical structure of the Internet, with densely connected core networks (red/orange) at the center and peripheral networks (blue/purple) at the edges. K-cores use degree-based decomposition while k-denses use triangle-based decomposition, highlighting different structural properties.

Input Format

Edge list (space or tab separated):

# Comments start with #
0 1
1 2
2 0

Weighted networks:

0 1 2.5
1 2 3.0

Supports .txt, .txt.gz, .txt.bz2 formats.

Common Options

Decomposition:

• --decomp [kcores|kdenses|dcores]: Decomposition algorithm (default: kcores)
• --weighted: Graph has edge weights
• --directed: Graph is directed (required for dcores)

Visualization:

• --width, --height: Image dimensions (default: 2400x2400)
• --background [black|white]: Background color (default: black)
• --epsilon FLOAT: Ring spread (default: 0.40)
• --edges-percent FLOAT: Percentage of edges to show (default: 0.5)
• --edge-alpha FLOAT: Edge transparency (default: 0.6)

Output:

• --output PATH: Visualization file (PNG, PDF, SVG)
• --cores-file PATH: Export decomposition data (CSV or JSON)

Full CLI reference: See docs/usage.md

Configuration

Generate a template:

lanet-vi config my_config.yaml

Example configuration:

visualization:
  background: black
  width: 2400
  height: 2400
  epsilon: 0.40
  edges_percent: 0.5
  edge_alpha: 0.6

layout:
  seed: 0

decomposition:
  decomp_type: kcores

Use it:

lanet-vi visualize --input network.txt --config my_config.yaml

Documentation

• K-Core Concepts - Understanding k-core decomposition
• Visualization Guide - How the plots work (colors, sizing, layout)
• Usage Guide - Detailed Python API and CLI examples
• Examples - Working examples with real datasets

Advanced Features

Community Detection

lanet-vi visualize --input network.txt \
  --detect-communities \
  --draw-community-boundaries \
  --output communities.png

Random Graph Generation

lanet-vi generate --output test.txt \
  --model barabasi-albert \
  --nodes 1000 --edges 3

D-Cores (Directed Networks)

lanet-vi visualize --input citations.txt \
  --directed --decomp dcores \
  --output dcores.png

Performance Tips

Large networks (>100K nodes):

config.visualization.edges_percent = 0.1  # Show 10% of edges
config.visualization.edge_alpha = 0.5
config.visualization.node_size_scale = 0.4

Publication quality:

config.visualization.width = 3600
config.visualization.height = 3600
config.visualization.background = "white"

Development

git clone https://github.com/conexdat/LaNet-vi.git
cd LaNet-vi
uv sync --all-extras
uv run pytest

Citation

If you use LaNet-vi in your research, please cite:

• Alvarez-Hamelin, J.I., Dall'Asta, L., Barrat, A., Vespignani, A. (2006). "Large scale networks fingerprinting and visualization using the k-core decomposition". Advances in Neural Information Processing Systems 18.

• Beiró, M.G., Alvarez-Hamelin, J.I., Busch, J.R. (2008). "A low complexity visualization tool that helps to perform complex systems analysis". New Journal of Physics.

License

MIT License

Authors

• Esteban Carisimo (Python implementation)
• Mariano Beiró (original C++ version)
• J. Ignacio Alvarez-Hamelin (original C++ version)