netsmith 0.2.1

Fast network analysis library with Rust acceleration

NetSmith: Fast Network Analysis Library

NetSmith is a high-performance network analysis library with Rust acceleration, focused on pure network analysis without time series dependencies.

Architecture

NetSmith follows a four-layer architecture:

Layer 1: Core

Pure math, no I/O, no global state. Located in src/netsmith/core/:

• graph.py: Core graph types (Graph, GraphView)
• metrics.py: Degree, centrality, assortativity, clustering, k-core, components
• paths.py: Shortest paths, reachability, walk metrics
• community.py: Modularity, Louvain hooks, label propagation hooks
• nulls.py: Null models and permutation tests
• stats.py: Distributions, confidence intervals, bootstrap

Layer 2: Engine

Performance and execution. Located in src/netsmith/engine/:

• python/: Reference Python implementations
• rust/: Rust-accelerated kernels (to be implemented)
• dispatch.py: Backend selection (auto, python, rust)
• contracts.py: Data contracts (EdgeList, GraphData)

Layer 3: API

Public surface. Located in src/netsmith/api/:

• load.py: Load edges from pandas, polars, parquet, csv
• graph.py: Public Graph API
• compute.py: Stable compute functions (degree, pagerank, communities)
• validate.py: Input validation

Layer 4: Apps

Opinionated use cases. Located in src/netsmith/apps/:

• cli/: Command-line interface
• reports/: HTML/markdown report generation
• datasets/: Sample graphs and download helpers

Data Contracts

Canonical edge representation:

EdgeList(
    u: NDArray[np.int64],      # Source nodes (length m)
    v: NDArray[np.int64],      # Destination nodes (length m)
    w: Optional[NDArray[np.float64]],  # Edge weights (optional)
    directed: bool,
    n_nodes: Optional[int]     # Preferred but inferred if not provided
)

Usage Examples

Basic Usage

import netsmith
import numpy as np

# Create edge list
u = np.array([0, 1, 2], dtype=np.int64)
v = np.array([1, 2, 0], dtype=np.int64)
edges = netsmith.api.load.EdgeList(u=u, v=v, directed=False, n_nodes=3)

# Compute degree
degrees = netsmith.degree(edges, backend="auto")
print(degrees)  # [2, 2, 2]

# Compute PageRank
pr = netsmith.pagerank(edges, alpha=0.85, backend="auto")
print(pr)

# Compute communities
communities = netsmith.communities(edges, method="louvain", backend="auto")
print(communities)

Loading from Files

# Load from parquet
edges = netsmith.load_edges("edges.parquet", u_col="source", v_col="target")

# Load from CSV
edges = netsmith.load_edges("edges.csv", u_col="u", v_col="v", w_col="weight")

CLI Usage

# Compute degree
netsmith compute degree --input edges.parquet --out degree.parquet

# Compute PageRank
netsmith compute pagerank --input edges.parquet --out pr.parquet --alpha 0.85

# Compute communities
netsmith compute communities --input edges.parquet --out communities.parquet

Installation

Minimal installation (numpy only - ~10MB):

pip install netsmith

With optional dependencies:

pip install netsmith[scipy]      # For sparse matrices (adjacency_matrix format='sparse'/'coo')
pip install netsmith[networkx]   # For community detection, null models, k-core decomposition
pip install netsmith[pandas]     # For pandas data loading
pip install netsmith[polars]     # For polars data loading

# Or install all optional dependencies:
pip install netsmith[scipy,networkx,pandas,polars]

# Development
pip install netsmith[dev]

Note: Core functionality (degree, paths, components, clustering) works with just numpy. scipy is only needed for sparse matrix formats, and networkx is only needed for advanced community detection and null models.

Rust Backend

The Rust backend will be automatically used if available. To build:

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Build and install
maturin develop --release

Status

✅ Completed:

• Core layer structure (graph types, metrics, paths, community, nulls, stats)
• Engine layer structure (Python backend, dispatch system, contracts)
• API layer (load, graph, compute, validate)
• Apps layer (CLI skeleton)
• pyproject.toml configuration

🚧 In Progress:

• Rust backend implementation
• Full metric implementations
• Community detection algorithms
• Test suite

📋 Planned:

• Rust acceleration for Phase 1 kernels (degree, strength, components, BFS, k-core)
• Comprehensive test coverage
• Documentation
• Performance benchmarks

Design Philosophy

NetSmith focuses on pure network analysis. For time series to network conversion, use downstream libraries that build on NetSmith.

License

MIT