🧅 LaNet-vi
Large-scale network visualization by k-core decomposition
LaNet-vi draws large networks so that their structure is readable at a glance: nodes are
placed in concentric rings by their k-core (or k-dense, or d-core) index, the densest core
at the center, with node size following the degree and colors following the index. It is
the Python version of the C++ LaNet-vi that produced the well-known Internet AS-level maps
(Alvarez-Hamelin, Dall'Asta, Barrat & Vespignani, NIPS 2005; Beiró, Alvarez-Hamelin &
Busch, New J. Phys. 2008), and since 5.1.0 it follows the original algorithms.
✨ Features
- 🧅 K-core, k-dense (m-core) and d-core decompositions, weighted k-cores by
strength intervals, all as in the C++ LaNet-vi
- 🎯 The LaNet-vi placement: nested components, rings by index, top-core cliques,
and the
pow/log circle packing of disconnected cores
- 🎨 The LaNet-vi look: rainbow or grayscale color scale, gradient edges, seeded edge
sampling, index and degree legends,
--window zoom, PNG/PDF/SVG output
- 🔗 K-connectivity of the shells (
--kconn), the analysis of Beiró, Alvarez-Hamelin
& Busch (2008), as in the C++ tool
- 📂 Plain edge lists (optionally weighted, directed, compressed) and CAIDA
AS-relationship snapshots
- 🐍 CLI and Python API with the same settings, also as a YAML file
- ⚙️ Tested on Python 3.11–3.13; the 78k-node AS graph renders in about a minute
📦 Installation
pip install lanet-vi
(or uv pip install lanet-vi). Python 3.11 or newer.
🚀 Quick Start
lanet-vi visualize --input network.txt --output network.png
import networkx as nx
from lanet_vi import LaNetConfig, Network
net = Network(nx.karate_club_graph(), LaNetConfig())
net.decompose()
net.visualize("karate.png")
The input is an edge list, one source target [weight] per line (# comments,
.gz/.bz2 accepted):
0 1
1 2 2.5
2 0
The Internet topology
from lanet_vi import LaNetConfig, Network
from lanet_vi.io.readers import read_caida_snapshot
graph, _ = read_caida_snapshot(
"https://publicdata.caida.org/datasets/as-relationships/serial-1/20251001.as-rel.txt.bz2"
)
net = Network(graph, LaNetConfig())
net.decompose()
net.visualize("internet.png")
examples/ has the full scripts behind the pictures above.
⚙️ Common Options
--decomp kcores|kdenses|dcores — the decomposition (dcores needs --directed)--weighted — the third column is a weight: strength-based cores--edges-percent 0.1 — fraction of edges drawn (default 0.5, never below --min-edges)--background white — or black (default); --color-scheme col|bw|bwi--width 3200 --height 2400 — any size and aspect ratio--window 0.25 0.75 0.25 0.75 — zoom: the central half of the picture at full size--coord-distribution pow — circle packing of disconnected cores (default: classic rings)--seed 42 — reproducible layout and edge sample--detect-communities — Louvain communities colored and outlined on the picture (a 5.x addition)--cores-file cores.csv — also write the decomposition (CSV, or JSON by extension)--config settings.yaml — settings from a file; explicit flags override it
lanet-vi config settings.yaml writes a template with every setting and its default;
lanet-vi info network.txt prints the network statistics; lanet-vi generate makes
random graphs to try things on.
📖 Documentation
The full documentation is at https://conexdat.github.io/LaNet-vi/ (the same pages
as docs/, with search and an API reference generated from the docstrings):
- Usage guide — every CLI option, the configuration file, the Python API
- Visualization guide — how the picture is built: placement, colors, sizes, edges, legends
- Concepts — k-cores, weighted k-cores, k-denses, k-connectivity, d-cores
- Coming from the C++ LaNet-vi — flag translation and what differs
- Examples — the CAIDA scripts
🛠️ Development
git clone https://github.com/CoNexDat/LaNet-vi.git
cd LaNet-vi
uv sync --all-extras
uv run pre-commit install
uv run pytest
Contributing
Contributions are welcome. main is protected: open a pull request and iterate until CI
and the automatic Copilot review are green. See CONTRIBUTING.md for the
full workflow, coding conventions and release process, and SECURITY.md for
reporting vulnerabilities.
Citation
If you use LaNet-vi in your research, please cite the software (GitHub's Cite this
repository button uses CITATION.cff) and the papers behind the method:
-
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.
🏛️ Heritage
LaNet-vi 5.x is a from-scratch Python rewrite of the original LaNet-vi (Large Network
visualization tool), a C++ program developed since 2005 by Mariano G. Beiró and J. Ignacio
Alvarez-Hamelin (Universidad de Buenos Aires / CONICET) together with Alain Barrat, Luca
Dall'Asta and Alessandro Vespignani. The C++ tool introduced the concentric k-core layout
this package is built on and produced, among others, the Internet AS-level maps that made
the method known.
License
The Python implementation is released under the MIT License, with the original
authors of the C++ version as co-holders of the copyright. The original C++ LaNet-vi remains
available under the Academic Free License 3.0 on SourceForge.
Authors
- Esteban Carisimo (Python implementation)
- Mariano G. Beiró (original C++ version)
- J. Ignacio Alvarez-Hamelin (original C++ version)
