AegisGraph: High-Performance Graph Library in plain C++ for Python

📚 Introduction

AegisGraph is a high-performance graph library written in C++ with Python bindings. It is designed to provide a fast and efficient alternative to popular graph libraries such as:

• NetworkX – Graph analysis library in Python.
• PyG (PyTorch Geometric) – Deep learning framework for graph-based learning.

AegisGraph focuses on hardware efficiency, memory optimization, and security, offering superior speed and scalability in graph operations.

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🎯 Why Choose AegisGraph?

🔥 Key Advantages

• Adjacency List Representation:
  Optimized graph storage that enhances memory efficiency and query speed compared to sparse matrix representations.
• C++ Backend for Maximum Performance:
  Core operations are implemented in C++ with minimal overhead, ensuring faster graph processing.
• Secure and Efficient Random Walks:
  Utilizes Pseudorandom Number Generators (PRNGs) instead of Python’s random.choice() for secure and reproducible random walks.
• Optimized RAM Usage:
  Memory-efficient graph loading and neighbor queries for handling large-scale graphs.
• No PyTorch Dependency:
  Lightweight deployment without heavy dependencies, making it ideal for various environments.

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⚡ Core Features

• 📡 Graph Construction: Supports directed, undirected, and weighted graphs.
• 🔄 Random Walks with PRNG: Secure and hardware-efficient random walk implementation.
• 🕸️ Graph Algorithms: Supports BFS, DFS, Dijkstra’s, and other common algorithms.
• 🧠 Fast Neighbor Queries: Constant-time neighbor access through adjacency lists.
• 📊 Graph Serialization: Save and load graphs efficiently.

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🛠️ Installation

📦 Prerequisites

• C++ Compiler: GCC, Clang, or MSVC supporting C++17 or later.
• Python (Optional for Bindings): Version 3.10 or higher.
• CMake: For building the library.

⚙️ Building from Source

1. Clone the Repository

git clone https://github.com/your-username/aegisgraph.git
cd aegisgraph
mkdir build
cd build
cmake ..
make

2. Install the Python Bindings

🐍 Python Usage

import aegisgraph

# Create a graph instance
graph = aegisgraph.Graph()

# Add edges
graph.add_edge(0, 1)
graph.add_edge(1, 2)
graph.add_edge(2, 3)
graph.add_edge(3, 0)

graph.getNeighbor(1)

# Perform a random walk
path = graph.random_walk(0, 10)
print(f"Random walk path: {path}")

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Install with pip

python3 -m venv .aegisgraph-env
source .aegisgraph-env/bin/activate
pip install --upgrade pip build
pip install -e .

🚀 Graph Loading Speed AegisGraph achieves a 3x speed improvement in loading large-scale graphs compared to NetworkX.

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📄 API Documentation 🚀 Graph Class

• addEdge(int u, int v, double weight = 1.0) – Add an edge between nodes.

• getNeighbors(int node) – Retrieve neighbors of a node.

• randomWalk(int start, int steps) – Perform a random walk starting from a node.

🔍 Utility Functions

• loadGraph(string filename) – Load graph from a file.

• saveGraph(string filename) – Save graph to a file.

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🐞 Reporting Issues To report a bug or request a feature, please open an issue .

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📫 Contact For questions, suggestions, or contributions, reach out via:

📧 Email: ldanielfch@gmail.com

🌐 GitHub: TheRadDani

📚 View Full API Documentation (HTML)

📖 Citation

If you use AegisGraph in academic work, please cite it as:

@misc{aegisgraph_2025,
  author       = {Daniel F. C.}, 
  title        = {AegisGraph: High-Performance Graph Library in plain C++ for Python},
  year         = {2025},
  howpublished = {\url{https://github.com/TheRadDani/aegisgraph}},
  note         = {Software library for efficient graph processing}
}