• Code
  • Highlights (Code)
  • Project Map
• Datasets
  • Highlights (Datasets)
  • Catalog
• Install & Quickstart
  • Python (per project)
  • R (per package)
• Development Guide
  • Python dev setup
  • R dev setup
• License
• Contact

• Dynamical models on networks: SIS/MM/POP ODEs, logistic/Hénon/Lorenz/Rössler maps, Motter–Lai cascading failures, diffusion processes, potential-driven random walks.
• Spectral & diffusion geometry: spectral entropies, Jensen–Shannon distances, diffusion distances, random-walk geometry for functional clustering.
• Information flow & inference: transfer entropy, correlation & temporal distance matrices.
• Robustness & dismantling: static/targeted attacks, OAD model for local/nonlocal failure propagation, functional robustness metrics.
• Multilayer: multilayer centralities, supra-adjacency, random walks.
• Production-friendly packaging: modern pyproject.toml, CLI entry points, Roxygen2 docs, unit tests, and reproducible examples.

Icon	Area	Project	Language	One‑liner
	Reservoir Computing	bioRC	R & Python	Bio-inspired reservoir computing on empirical & synthetic connectomes.
	Brain Network Analysis	connectome-dimension	Python	Load, preprocess, threshold, and analyze human connectome data across parcellations and subject groups for dimension-based analysis.
	Symbolic Dynamics & Dimensionality	embedding-dim	Python	Estimate embedding dimension via symbolic entropy, redundancy, and predictability analysis of time series.
	Migration Modeling	FERM	Python	A modular framework for simulating agent-based migration flows using niche-population maps and spatial constraints.
	Time Series Forecasting & Entropy	forecasting	Python	Modular toolkit for SARIMAX forecasting with exogenous/endogenous drivers and entropy rate estimation using Lempel-Ziv methods.
	Robustness & Resilience	functional-robustness	Python	Simulates network dismantling via classical and entropy-based centralities to quantify functional resilience under targeted attacks.
	Infodemic Mapping	infodemap	Python	Visualizes Infodemic Risk Index (IRI) across World, USA, and EU/Italy regions using geospatial data and COVID-era misinformation metrics
	Mesoscale Network Dynamics	jacobian_geometry	Python	Modular library to extract functional mesoscale structure from dynamics on networks using Jacobian-based distance metrics
	Multilayer Network Analysis	MuxVizPy	Python	Modular Python port of MuxViz for building, analyzing, and visualizing edge-colored multilayer networks with centrality, percolation, and SBM community detection.
	Network Thermodynamics	NDM	Python	Simulates diffusion processes on networks and extracts thermodynamic observables like entropy and free energy from density matrices.
	Systemic risk & cascades	OAD	Python	Gillespie-simulated Operational–Affected–Disrupted cascades with local and global-field spreading on networks; outputs survivors and GCC fraction.
	Dynamical Systems on Networks	perturbNet	Python	Analyze perturbation propagation in steady-state ODE models on networks using correlation matrices, temporal distances, and concentric visualizations.
	Epidemic Modeling	SEIR-deniers	Python	SEIR-deniers simulates epidemic spreading with behavioral heterogeneity, modeling Deniers and Cooperators in a compartmental SEIR framework.
	Network Robustness	structural-robustness	Python	Analyze how different centrality-based node removal strategies impact the structural robustness of complex networks using spectral entropy and entanglement.
	Collective Dynamics	collectiveDyn	R	Simulation and analysis of coupled dynamical systems on networks, with map- and ODE-based models, adaptive rewiring, and multi-time-series utilities.
	Multilayer Networks	MultiNetLib	R	Modular tools for multilayer/multiplex analysis—supra-adjacency builders, spectral geometry & entropy, random walks, and robustness profiles.
	Network Growth Models	NetGrowLib	R	Library of generative network models (BA, CHKNS, age-biased, clustered) with tools for degree distributions and growth analysis.
	Network Robustness	RobustnessProfiles	R	Tools to simulate node removals, build robustness profiles, and visualize critical resilience thresholds of complex networks under targeted and random attacks.
	Network Information Theory	SpectralEntropyLib	R	Tools for quantifying and comparing complex networks using spectral entropies, divergences, and information-theoretic distances.
	Diffusion Geometry	SpectralGeometry	R	Spectral-geometry tools for graphs—Laplacians, spectra, heat kernels, diffusion distances, and embeddings to reveal functional clusters.
	Epidemic & Opinion Dynamics	StochasticDynamics	R	Simulates and visualizes stochastic spreading, opinion, and reaction–diffusion processes on networks with 3D rendering and video export.

• Breadth and scope: Curated, domain-spanning collection covering social–ecological exchanges, biomedical interactomes, neuronal connectomes, genetic/protein systems, coauthorship, transport, trade, and microbiomes.
• Multiplex structure and dynamics: Explicit layer semantics (2–364+ layers), directed/weighted edges, and temporal annotations where available—suited to multilayer centralities, diffusion models, and time-resolved analyses.
• Standardized, interoperable formats: Extended edgelists with layer indices and consistent naming; readily ingestible in Python/R/graph-tool/Gephi pipelines, with repository and DOI links per entry.
• Benchmarking across domains: Event-centric Twitter multiplexes and canonical social networks enable comparable tests for attention dynamics and information diffusion; other domains support cross-domain benchmarking.
• Analysis coverage: Supports disease reclassification (gene–symptom multiplexes), transport resilience and disruption modeling, trade flow analysis, coauthorship structure, and microbiome network inference.
• Provenance and compliance: Each dataset traces to maintained sources or peer-reviewed publications for transparent citation and reproducibility; social data use anonymized identifiers and adhere to platform/data-use policies.

All Python subprojects follow a modern pyproject.toml layout and can be installed either locally (dev mode) or as a user package.

Local editable install

# inside a given Python project folder
python -m venv .venv && source .venv/bin/activate   # optional but recommended
pip install -U pip
pip install -e .

Each R package is Roxygen2‑documented and devtools‑friendly.

# from inside the R package folder
install.packages(c("devtools","roxygen2","testthat"), dependencies = TRUE)
devtools::document()  # generate Rd + NAMESPACE
devtools::install()   # install locally
devtools::test()      # run unit tests

• Structure: src/<package_name>/, tests/, pyproject.toml, README.md.
• Dependencies: keep runtime deps minimal; move extras to optional-dependencies.
• Style: NumPy‑style docstrings.
• Testing: pytest; use small, deterministic fixtures.
• Docs: pdoc or Sphinx; provide a docs/ quickstart with examples and API references.

• Structure: R/, man/, tests/testthat/, DESCRIPTION, NAMESPACE, README.md.
• Documentation: Roxygen2 with @examples, @returns, @seealso.
• Testing: testthat; keep tests fast and focused.

Unless specified otherwise in a subproject, the default license is MIT.
Individual subprojects may differ (e.g., GPL‑3.0 for R packages). See each LICENSE/DESCRIPTION.

For information, pull requests, and other inquiries, contact Prof. Manlio De Domenico and Andrea Valsecchi:

• Pull Request
• General Information