temporal-walk 0.5.9

A library to sample temporal walks from in-memory temporal graphs

🚀 Temporal Walk

A high-performance temporal walk sampler for dynamic networks with GPU acceleration. Built for scale.

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🔥 Why Temporal Walk?

✅ Performance First – GPU-accelerated sampling for massive networks (development in progress)
✅ Memory Efficient – Smart memory management for large graphs
✅ Flexible Integration – Easy Python bindings with NumPy/NetworkX support
✅ Production Ready – Tested with hundreds of extensive unit tests.

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⚡ Quick Start

from temporal_walk import TemporalWalk

# Create a directed temporal graph
walker = TemporalWalk(is_directed=True, use_gpu=False)

# Add edges: (source, target, timestamp)
edges = [
    (4, 5, 71), (3, 5, 82), (1, 3, 19),
    (4, 2, 34), (4, 3, 79), (2, 5, 19)
]
walker.add_multiple_edges(edges)

# Sample walks with exponential time bias
walks = walker.get_random_walks_for_all_nodes(
    max_walk_len=5,
    walk_bias="ExponentialIndex",
    num_walks_per_node=10,
    initial_edge_bias="Uniform"
)

✨ Key Features

• ⚡ GPU acceleration for large graphs (development in progress)
• 🎯 Multiple sampling strategies – Uniform, Linear, Exponential
• 🔄 Forward & backward temporal walks
• 📡 Rolling window support for streaming data
• 🔗 NetworkX integration
• 🛠️ Efficient memory management

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📦 Installation

pip install temporal-walk

📖 Documentation

📌 C++ Documentation →
📌 Python Interface Documentation →

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📚 Inspired By

Nguyen, Giang Hoang, et al.
"Continuous-Time Dynamic Network Embeddings."
Companion Proceedings of The Web Conference 2018.

👨‍🔬 Built by Packets Research Lab

🚀 Contributions welcome! Open a PR or issue if you have suggestions.