Dynamic Exploration Graph (DEG)

High-throughput approximate nearest neighbor and exploratory search library implementing continuous edge optimization and dynamic stream indexing (MMM '25, ICMR '24).

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• C++20 header-only library with native Python bindings (deglib)s
• Dynamic streaming: Incremental addition, removal, and continuous edge optimization
• Multi-threaded construction and batch vector search with SIMD acceleration (AVX2, AVX-512)
• Supported data types: float32, float16, int8, uint8, evp-bits
• Supported metrics: Euclidean ($L_2$), Inner Product / Cosine, quantized EVP
• Exploratory graph traversal and label-filtered nearest neighbor search
• Compact graph serialization and lightweight read-only deployment mode

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Getting Started

Python

Install the module via pip:

pip install deglib

Build a search graph and query nearest neighbors:

import numpy as np
import deglib

# 10,000 vectors with 128 dimensions
data = np.random.randn(10_000, 128).astype(np.float32)
query = np.random.randn(128).astype(np.float32)

# 1. Build search index directly from data
graph = deglib.builder.build_from_data(data, metric=deglib.Metric.FP32_L2)

# 2. Search top-10 nearest neighbors
indices, distances = graph.search(query, k=10, eps=0.1)


print("Top-10 neighbor IDs:", indices)
print("Distances:", distances)

# 3. Save graph for serving
graph.save_graph("index.deg")

For more Python examples, check the examples/ directory or read the Official Documentation.

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C++

deglib is a header-only C++20 library. Simply add the cpp/deglib/include directory to your project:

#include <deglib/deglib.h>
#include <iostream>
#include <vector>
#include <random>

int main() {
    const uint32_t num_vectors = 10'000;
    const uint32_t dims = 128;

    // Generate example feature dataset
    std::mt19937 rng(42);
    std::uniform_real_distribution<float> dist(0.0f, 1.0f);
    std::vector<float> dataset(num_vectors * dims);
    for (auto& val : dataset) val = dist(rng);

    // Build graph index directly from data
    auto graph = deglib::build_from_data(
        std::span<const float>(dataset),
        dims,
        /*labels=*/{},
        /*edges_per_vertex=*/32,
        deglib::distances::Metric::FP32_L2
    );

    // Query top-10 nearest neighbors
    std::vector<float> query(dims);
    for (auto& val : query) val = dist(rng);

    auto results = graph.search(std::span<const float>(query), /*k=*/10, /*eps=*/0.1f);

    for (const auto& match : results) {
        std::cout << "Label: " << match.getIdentifier() 
                  << " | Distance: " << match.getDistance() << "\n";
    }
}

For full C++ build instructions, CMake presets, and architecture details, refer to the cpp/ README.

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Repository Structure

DynamicExplorationGraph/
├── cpp/          # High-performance C++20 Header-Only library, CMake Presets, Tests & Benchmarks
├── python/       # Python Bindings (deglib), Pytest Suite & Wheel Build Configuration
├── examples/     # Ready-to-run Python examples (knng, mips, static_data, vibe, dynamic_data, sliding_window)
├── java/         # Java implementation & Benchmarks
└── docs/         # Sphinx / ReadTheDocs Documentation

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Performance

Approximate Nearest Neighbor Search (ANNS): Querying unindexed vectors across various graph exploration margins ($\epsilon$).

Exploratory Search (Indexed Queries): Navigating from existing indexed vertices to discover immediate neighbor clusters.

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Datasets & Pre-built Graphs

The following standard datasets and pre-built graph files are supported in benchmarks and examples/ :

Dataset	Dimension	Base Vectors	Query Vectors	Pre-built Graph	Reference
SIFT1M	128	1,000,000	10,000	sift_128D_L2_DEG30.deg	Texmex
DEEP1M	96	1,000,000	10,000	deep1m_96D_L2_DEG30.deg	PPUDA
GloVe-100	100	1,183,514	10,000	glove_100D_L2_DEG30.deg	Stanford GloVe
Audio	192	53,387	200	Auto-generated	Princeton CASS
Enron	1,369	94,987	200	Auto-generated	CMU Enron

Note

When executing benchmarks or Python examples/ , datasets are automatically downloaded and prepared on first run.

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Citation

If you use the library in an academic context, please consider citing our papers:

Hezel, N., Barthel, K.U., Schilling, B., Schall, K., Jung, K. Dynamic Exploration Graph: A Novel Approach for Efficient Nearest Neighbor Search in Evolving Multimedia Datasets. MultiMedia Modeling (MMM 2025): 333–347.

@article{Hezel2025,
  author    = {Hezel, Nico and Barthel, Uwe Kai and Schilling, Bruno and Schall, Konstantin and Jung, Klaus},
  title     = {Dynamic Exploration Graph: A Novel Approach for Efficient Nearest Neighbor Search in Evolving Multimedia Datasets},
  booktitle = {MultiMedia Modeling},
  publisher = {Springer Nature},
  pages     = {333--347},
  isbn      = {978-981-96-2054-8},
  year      = {2025}
}

Hezel, N., Barthel, K.U., Schall, K., Jung, K. An Exploration Graph with Continuous Refinement for Efficient Multimedia Retrieval. Proceedings of the 2024 International Conference on Multimedia Retrieval (ICMR '24): 657–665.

@inproceedings{Hezel2024,
  author    = {Hezel, Nico and Barthel, Uwe Kai and Schall, Konstantin and Jung, Klaus},
  title     = {An Exploration Graph with Continuous Refinement for Efficient Multimedia Retrieval},
  booktitle = {Proceedings of the 2024 International Conference on Multimedia Retrieval},
  publisher = {Association for Computing Machinery},
  pages     = {657--665},
  isbn      = {9798400706196},
  doi       = {10.1145/3652583.3658117},
  series    = {ICMR '24},
  year      = {2024}
}

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License

DEG is available under the MIT License.