tempest-anonymous 1.4.4

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

🚀 Tempest

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

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🔥 Why Tempest?

✅ Performance First – GPU-accelerated sampling for massive networks
✅ 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.
✅ Multi Platform Builds and runs seamlessly on devices with or without CUDA.

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⚡ Quick Start

from tempest import Tempest

# Create a directed temporal graph
walker = Tempest(is_directed=True, use_gpu=True, max_time_capacity=-1)

# Add edges - can be numpy arrays or python lists
sources = [3, 2, 0, 3, 3, 1]
targets = [4, 4, 2, 1, 2, 4]
timestamps = [71, 82, 19, 34, 79, 19]

walker.add_multiple_edges(sources, targets, timestamps)

# Sample walks with exponential time bias
walk_nodes, walk_timestamps, walk_lens = walker.get_random_walks_and_times_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
• 🎯 Multiple sampling strategies – Uniform, Linear, Exponential
• 🔄 Forward & backward temporal walks
• 📡 Rolling window support for streaming data
• 🔗 NetworkX integration
• 🛠️ Efficient memory management
• ⚙️ Uses C++ std libraries or Thrust API selectively based on hardware availability and configuration.

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📦 Dependencies

Dependency	Purpose
pybind11	Python-C++ bindings
python3	Required for building the python interfaces
gtest	Unit testing framework

💡 Tip: Use vcpkg to easily install and link the C++ dependencies.

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📦 Installation

pip install tempest-anonymous

📖 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.