NetGraph-Core

C++ graph engine for network flow analysis, traffic engineering simulation, and capacity planning.

Overview

NetGraph-Core provides a specialized graph implementation for networking problems. Key design priorities:

Determinism: Guaranteed reproducible edge ordering by (cost, src, dst).
Flow Modeling: Native support for multi-commodity flow state, residual tracking, and ECMP/WCMP placement.
Performance: Immutable CSR (Compressed Sparse Row) adjacency, and zero-copy NumPy views over mutable flow state.

Core Features

1. Graph Representations

StrictMultiDiGraph: Immutable directed multigraph using CSR adjacency. Supports parallel edges (multi-graph), essential for network topologies.
FlowGraph: Topology overlay managing mutable flow state, per-flow edge allocations, and residual capacities.

2. Network Algorithms

Shortest Paths (SPF):
- Modified Dijkstra returns a Predecessor DAG to capture all equal-cost paths.
- Supports ECMP (Equal-Cost Multi-Path) routing.
- Features node/edge masking and residual-aware tie-breaking.
K-Shortest Paths (KSP):
- Yen's algorithm returning DAG-wrapped paths.
- Configurable constraints on cost factors (e.g., paths within 1.5x of optimal).
Explicit Paths:
- PredDAG.from_edges(graph, edges) builds a path bundle from an operator-supplied edge sequence, usable anywhere a PredDAG is accepted.
Max-Flow:
- Algorithm: Iterative augmentation using Successive Shortest Path on residual graphs, pushing flow across full ECMP/WCMP DAGs at each step. For Proportional placement with require_capacity=True, a final residual completion phase augments with reverse arcs so the result is a true maximum flow; the other modes are placement models and may report less.
- Traffic Engineering (TE) Mode: Routing adapts to residual capacity (progressive fill).
- IP Routing Mode: Cost-only routing (ECMP/WCMP) ignoring capacity constraints.
Analysis:
- Sensitivity Analysis: Identifies critical edges by removing each saturated edge and measuring how much total flow is lost. Supports shortest_path mode to analyze only edges used under ECMP routing (IP/IGP networks) vs. full max-flow (SDN/TE networks).
- Min-Cut: Computes minimum cuts on residual graphs.

3. Flow Policy Engine

Unified configuration object (FlowPolicy) that models diverse routing behaviors:

Modeling: Unified configuration for IP Routing (static costs) and Traffic Engineering (dynamic residuals).
Placement Strategies:
- EqualBalanced: ECMP (equal splitting) - equal distribution across next-hops and parallel edges.
- Proportional: WCMP (weighted splitting) - distribution proportional to residual capacity.
Lifecycle Management: Handles demand placement, re-optimization of existing flows, and constraints (path cost, stretch factor, flow counts).
Static (Pinned) Paths: FlowPolicy.set_static_paths() pins a demand to explicit path bundles (MPLS-style). One flow per usable bundle; a bundle with no path surviving the failure masks is down and carries nothing, since a pinned path does not reroute.

4. Python Integration

Zero-Copy: FlowState and FlowGraph *_view() methods expose C++ buffers as read-only NumPy arrays that track mutation in place. StrictMultiDiGraph.*_view() returns copies instead, so the graph's immutability cannot be violated.
Concurrency: Releases the Python GIL during the long-running algorithms (SPF, KSP, max-flow, placement) to enable threading. PredDAG.resolve_to_paths holds the GIL. batch_max_flow and sensitivity_analysis also use internal worker threads, sized by NGRAPH_CORE_BATCH_THREADS / NGRAPH_CORE_SENSITIVITY_THREADS.

Quick Start

import numpy as np
import netgraph_core as ngc

# Two parallel paths from node 0 to node 3.
graph = ngc.StrictMultiDiGraph.from_arrays(
    num_nodes=4,
    src=np.array([0, 1, 0, 2], dtype=np.int32),
    dst=np.array([1, 3, 2, 3], dtype=np.int32),
    capacity=np.array([10.0, 10.0, 5.0, 5.0], dtype=np.float64),
    cost=np.array([1, 1, 1, 1], dtype=np.int64),
    ext_edge_ids=np.arange(4, dtype=np.int64),  # your own stable edge ids
)

algs = ngc.Algorithms(ngc.Backend.cpu())
handle = algs.build_graph(graph)

total, summary = algs.max_flow(handle, 0, 3)
print(total)                            # 15.0
print(summary.min_cut.edges.tolist())   # [0, 1] -- bottleneck edge ids

Installation

pip install netgraph-core

Or from source:

pip install -e .

Build Optimizations

Default builds include LTO and loop unrolling. For local development:

make install-native   # CPU-specific optimizations (not portable)

Repository Structure

src/                    # C++ implementation
include/netgraph/core/  # Public C++ headers
bindings/python/        # pybind11 bindings
python/netgraph_core/   # Python package
tests/cpp/              # C++ tests (googletest)
tests/py/               # Python tests (pytest)

Development

make dev        # Setup: venv, dependencies, pre-commit hooks
make check      # Run all tests and linting (auto-fix formatting)
make check-ci   # Strict checks without auto-fix (for CI)
make test       # Python tests
make cpp-test   # C++ tests only
make cov        # Combined coverage report (C++ + Python)

Environment Variables

Variable	Effect
`NGRAPH_CORE_PROFILE=1`	Enable profiling of C++ hot paths (`profiling_dump()` / `profiling_reset()`).
`NGRAPH_CORE_BATCH_THREADS`	Worker threads for `batch_max_flow` (default: hardware concurrency). Set to `1` when calling from your own worker pool.
`NGRAPH_CORE_SENSITIVITY_THREADS`	Worker threads for `sensitivity_analysis` (default: hardware concurrency).

Requirements

C++: C++20 compiler (GCC 10+, Clang 12+, MSVC 2019+)
Python: 3.11+
Build: CMake 3.23+, scikit-build-core
Dependencies: pybind11, NumPy

License

MIT License

Metadata

Release files for netgraph-core 0.9.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for netgraph-core 0.9.0
File	Size	Uploaded
netgraph_core-0.9.0.tar.gz	210.7 kB	Sep 12, 2026	Details

Built distributions (wheels)

Table of built distributions (wheels) for netgraph-core 0.9.0
File	Interpreter	ABI	Platform	Reset
netgraph_core-0.9.0-cp314-cp314t-win_amd64.whl 356.5 kB Sep 12, 2026	CPython 3.14	CPython 3.14 free-threading	Windows x86-64	Details
netgraph_core-0.9.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 522.6 kB Sep 12, 2026	CPython 3.14	CPython 3.14 free-threading	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
netgraph_core-0.9.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 491.5 kB Sep 12, 2026	CPython 3.14	CPython 3.14 free-threading	Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64	Details
netgraph_core-0.9.0-cp314-cp314t-macosx_10_15_universal2.whl 673.2 kB Sep 12, 2026	CPython 3.14	CPython 3.14 free-threading	macOS 10.15+ universal2 (ARM64, x86-64)	Details
netgraph_core-0.9.0-cp314-cp314-win_amd64.whl 347.3 kB Sep 12, 2026	CPython 3.14	CPython 3.14	Windows x86-64	Details
netgraph_core-0.9.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 525.4 kB Sep 12, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
netgraph_core-0.9.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 497.5 kB Sep 12, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64	Details
netgraph_core-0.9.0-cp314-cp314-macosx_10_15_universal2.whl 641.3 kB Sep 12, 2026	CPython 3.14	CPython 3.14	macOS 10.15+ universal2 (ARM64, x86-64)	Details
netgraph_core-0.9.0-cp313-cp313-win_amd64.whl 337.0 kB Sep 12, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
netgraph_core-0.9.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 524.6 kB Sep 12, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
netgraph_core-0.9.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 493.7 kB Sep 12, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64	Details
netgraph_core-0.9.0-cp313-cp313-macosx_10_13_universal2.whl 640.2 kB Sep 12, 2026	CPython 3.13	CPython 3.13	macOS 10.13+ universal2 (ARM64, x86-64)	Details
netgraph_core-0.9.0-cp312-cp312-win_amd64.whl 336.9 kB Sep 12, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
netgraph_core-0.9.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 524.8 kB Sep 12, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
netgraph_core-0.9.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 493.8 kB Sep 12, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64	Details
netgraph_core-0.9.0-cp312-cp312-macosx_10_13_universal2.whl 640.3 kB Sep 12, 2026	CPython 3.12	CPython 3.12	macOS 10.13+ universal2 (ARM64, x86-64)	Details
netgraph_core-0.9.0-cp311-cp311-win_amd64.whl 333.3 kB Sep 12, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
netgraph_core-0.9.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 509.9 kB Sep 12, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
netgraph_core-0.9.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 487.0 kB Sep 12, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64	Details
netgraph_core-0.9.0-cp311-cp311-macosx_10_9_universal2.whl 632.8 kB Sep 12, 2026	CPython 3.11	CPython 3.11	macOS 10.9+ universal2 (ARM64, x86-64)	Details

Total release size: 10.2 MB

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Publishing commit: github.com/networmix/NetGraph-Core/tree/07de60d71fb2f7f26d78ebb5ba3131e169674fb7

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Publishing commit: github.com/networmix/NetGraph-Core/tree/07de60d71fb2f7f26d78ebb5ba3131e169674fb7

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Provenance

Identity

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Publishing repository: github.com/networmix/NetGraph-Core
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