nimnet

A comprehensive network science library for Nim, inspired by Python’s NetworkX.

Get started API Reference View on GitHub

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Why nimnet?

Performance — Nim compiles to C with zero-overhead abstractions. nimnet leverages cached edge counts (O(1)), func/{.inline.} annotations, and pre-sized allocations for serious speed.

Ergonomics — Nim-idiomatic API with operator overloading (in, [], +, -), iterators (for n in g), and batch operations (addEdgesFrom).

Extensibility — Generic node types (Graph[int], Graph[string], or any hashable type), typed edge tuples, and a modular architecture.

Quick example

import nimnet

var g = newGraph[int]()
g.addEdgesFrom([(1, 2), (2, 3), (3, 1)])

assert 1 in g
assert g.len == 3
echo g  # Graph(nodes=3, edges=3)

for node in g:
  echo node, " has degree ", g.degree(node)

Features at a glance

Category	Modules
Core	Graph[N], DiGraph[N], MultiGraph[N], MultiDiGraph[N], generic node types, cached metrics
Algorithms (61 modules)	Traversal, shortest paths, centrality, communities, connectivity, flow, matching, spectral analysis, layouts, approximation and network analysis. Includes exact cardinality matching, Hopcroft-Karp and sparse undirected all-pairs paths introduced in 1.1.0.
Generators (21 modules)	Classic, random, sparse G(n,p), small/famous, tree, lattice, geometric, community, degree-sequence, directed, duplication, expander, Harary, internet, intersection, joint-degree, Mycielski, non-isomorphic tree, stochastic, triad and miscellaneous generators.
I/O (13 modules)	Edge list, adjacency list, multiline adjacency list, JSON graph, DOT, GML, GraphML, GEXF, Graph6/Sparse6, Pajek, LEDA, network text and SVG. Some formats are export-only.
Operators	Union, complement, compose, intersection, difference, disjoint union, node relabeling, directed ↔ undirected conversion
Other	Builder DSL, graph views (lazy subgraph/filter), CompactGraph (CSR), StaticGraph (compile-time), built-in datasets (dolphins, Florentine families, les misérables), adjacency matrix/edge list conversion, parallel algorithms (malebolgia threading)

Performance

Performance varies by algorithm, graph and hardware. The benchmark suite uses shared graph/weight fixtures, equivalent operations, monotonic elapsed timing, sequential runs and recorded environments. Historical tables using different random graphs and timing methods have been retired.

The improvement backlog records the changes introduced in 1.1.0 and their delivery evidence.