temporal-random-walk 1.6.2

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

🚀 Temporal Random Walk

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

---

🔥 Why Temporal Random Walk?

✅ 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.

---

⚡ Quick Start

from temporal_random_walk import TemporalRandomWalk

# Create a directed temporal graph
walker = TemporalRandomWalk(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, edge_features = 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"
)
# edge_features is None when no edge features were added (feature_dim=0)

✨ Key Features

• ⚡ GPU acceleration for large graphs
• 🎯 Multiple sampling strategies – Uniform, Linear, Exponential
• 🧠 Advanced temporal biases – ExponentialWeight (CTDNE-style) NeurTWs (SpatioTemporal bias) and TemporalNode2Vec
• 🔄 Forward & backward temporal walks
• 📡 Rolling window support for streaming data
• 🏷️ Optional edge feature propagation from input edges to sampled walks
• 🔗 NetworkX integration
• 🛠️ Efficient memory management
• ⚙️ Uses C++ std libraries or Thrust API selectively based on hardware availability and configuration.

---

🏷️ Edge Features (Optional)

If your edges carry attributes (weights, embeddings, types, etc.), you can pass them to add_multiple_edges(...) and receive aligned edge features for each sampled transition.

import numpy as np
from temporal_random_walk import TemporalRandomWalk

walker = TemporalRandomWalk(is_directed=True, use_gpu=False)

sources = np.array([0, 0, 1], dtype=np.int32)
targets = np.array([1, 2, 2], dtype=np.int32)
timestamps = np.array([10, 20, 30], dtype=np.int64)

# shape: [num_edges, feature_dim]
edge_features = np.array([
    [0.1, 1.0],
    [0.2, 0.5],
    [0.9, 0.3],
], dtype=np.float32)

walker.add_multiple_edges(sources, targets, timestamps, edge_features=edge_features)

walk_nodes, walk_timestamps, walk_lens, walk_edge_features = walker.get_random_walks_and_times(
    max_walk_len=4,
    walk_bias="Uniform",
    num_walks_total=5,
)

# walk_edge_features.shape == [num_walks, max_walk_len - 1, feature_dim]

walk_edge_features is None when no edge features are provided.

🏷️ Node Features

The library can also store dense node features. Use set_node_features(node_ids, node_features) to populate features for specific nodes, then get_node_features() to retrieve the dense matrix.

---

🧭 Bias Selection Notes

• Use ExponentialIndex or Linear for recency-aware sampling with no extra setup.
• Use ExponentialWeight when you want CTDNE-style weight computation (enable_weight_computation=True, optionally tune timescale_bound).
• Use SpatioTemporal for NeurTWs-style sampling that combines three dynamic biases during sampling: temporal recency, spatial preference for nodes with lower temporal degree, and an exploration penalty that discourages revisiting nodes.
• Use TemporalNode2Vec when you need return/in-out control via temporal_node2vec_p and temporal_node2vec_q.

---

📦 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.

---

📦 Installation

pip install temporal-random-walk

📖 Documentation

📌 C++ Documentation →
📌 Python Interface Documentation →

---

📚 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.