https://github.com/imoonlab/hypergraph-db
Hypergraph Database.
https://github.com/imoonlab/hypergraph-db
hypergraph hypergraph-neural-networks hypergraphs visualization
Last synced: 7 months ago
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Hypergraph Database.
- Host: GitHub
- URL: https://github.com/imoonlab/hypergraph-db
- Owner: iMoonLab
- License: apache-2.0
- Created: 2024-12-13T15:48:07.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-22T09:54:16.000Z (about 1 year ago)
- Last Synced: 2025-05-05T05:04:48.211Z (8 months ago)
- Topics: hypergraph, hypergraph-neural-networks, hypergraphs, visualization
- Language: Python
- Homepage:
- Size: 60.5 KB
- Stars: 20
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Hypergraph-DB
About |
Features |
Installation |
Starting |
License |
Contact |
Author
## :dart: About
Hypergraph-DB is a lightweight, flexible, and Python-based database designed to model and manage **hypergraphs**—a generalized graph structure where edges (hyperedges) can connect any number of vertices. This makes Hypergraph-DB an ideal solution for representing complex relationships between entities in various domains, such as knowledge graphs, social networks, and scientific data modeling.
Hypergraph-DB provides a high-level abstraction for working with vertices and hyperedges, making it easy to add, update, query, and manage hypergraph data. With built-in support for persistence, caching, and efficient operations, Hypergraph-DB simplifies the management of hypergraph data structures.
**:bar_chart: Performance Test Results**
To demonstrate the performance of **Hypergraph-DB**, let’s consider an example:
- Suppose we want to construct a **hypergraph** with **1,000,000 vertices** and **200,000 hyperedges**.
- Using Hypergraph-DB, it takes approximately:
- **1.75 seconds** to add **1,000,000 vertices**.
- **1.82 seconds** to add **200,000 hyperedges**.
- Querying this hypergraph:
- Retrieving information for **400,000 vertices** takes **0.51 seconds**.
- Retrieving information for **400,000 hyperedges** takes **2.52 seconds**.
This example demonstrates the efficiency of Hypergraph-DB, even when working with large-scale hypergraphs. Below is a detailed table showing how the performance scales as the size of the hypergraph increases.
**Detailed Performance Results**
The following table shows the results of stress tests performed on Hypergraph-DB with varying scales. The tests measure the time taken to add vertices, add hyperedges, and query vertices and hyperedges.
| **Number of Vertices** | **Number of Hyperedges** | **Add Vertices (s)** | **Add Edges (s)** | **Query Vertices (s/queries)** | **Query Edges (s/queries)** | **Total Time (s)** |
|-------------------------|--------------------------|-----------------------|-------------------|-------------------------------|----------------------------|--------------------|
| 5,000 | 1,000 | 0.01 | 0.01 | 0.00/2,000 | 0.01/2,000 | 0.02 |
| 10,000 | 2,000 | 0.01 | 0.01 | 0.00/4,000 | 0.02/4,000 | 0.05 |
| 25,000 | 5,000 | 0.03 | 0.04 | 0.01/10,000 | 0.05/10,000 | 0.13 |
| 50,000 | 10,000 | 0.06 | 0.07 | 0.02/20,000 | 0.12/20,000 | 0.26 |
| 100,000 | 20,000 | 0.12 | 0.17 | 0.04/40,000 | 0.24/40,000 | 0.58 |
| 250,000 | 50,000 | 0.35 | 0.40 | 0.11/100,000 | 0.61/100,000 | 1.47 |
| 500,000 | 100,000 | 0.85 | 1.07 | 0.22/200,000 | 1.20/200,000 | 3.34 |
| 1,000,000 | 200,000 | 1.75 | 1.82 | 0.51/400,000 | 2.52/400,000 | 6.60 |
---
**Key Observations:**
1. **Scalability**:
Hypergraph-DB scales efficiently with the number of vertices and hyperedges. The time to add vertices and hyperedges grows linearly with the size of the hypergraph.
2. **Query Performance**:
Querying vertices and hyperedges remains fast, even for large-scale hypergraphs. For instance:
- Querying **200,000 vertices** takes only **0.22 seconds**.
- Querying **200,000 hyperedges** takes only **1.20 seconds**.
3. **Total Time**:
The total time to construct and query a hypergraph with **1,000,000 vertices** and **200,000 hyperedges** is only **6.60 seconds**, showcasing the overall efficiency of Hypergraph-DB.
This performance makes **Hypergraph-DB** a great choice for applications requiring fast and scalable hypergraph data management.
---
## :sparkles: Features
:heavy_check_mark: **Flexible Hypergraph Representation**
- Supports vertices (`v`) and hyperedges (`e`), where hyperedges can connect any number of vertices.
- Hyperedges are represented as sorted tuples of vertex IDs, ensuring consistency and efficient operations.
:heavy_check_mark: **Vertex and Hyperedge Management**
- Add, update, delete, and query vertices and hyperedges with ease.
- Built-in methods to retrieve neighbors, incident edges, and other relationships.
:heavy_check_mark: **Neighbor Queries**
- Get neighboring vertices or hyperedges for a given vertex or hyperedge.
:heavy_check_mark: **Persistence**
- Save and load hypergraphs to/from disk using efficient serialization (`pickle`).
- Ensures data integrity and supports large-scale data storage.
:heavy_check_mark: **Customizable and Extensible**
- Built on Python’s `dataclasses`, making it easy to extend and customize for specific use cases.
---
## :rocket: Installation
Hypergraph-DB is a Python library. You can install it directly from PyPI using `pip`.
```bash
pip install hypergraph-db
```
You can also install it by cloning the repository or adding it to your project manually. Ensure you have Python 3.10 or later installed.
```bash
# Clone the repository
git clone https://github.com/iMoonLab/Hypergraph-DB.git
cd Hypergraph-DB
# Install dependencies (if any)
pip install -r requirements.txt
```
---
## :checkered_flag: Starting
This section provides a quick guide to get started with Hypergraph-DB, including iusage, and running basic operations. Below is an example of how to use Hypergraph-DB, based on the provided test cases.
#### **1. Create a Hypergraph**
```python
from hyperdb import HypergraphDB
# Initialize the hypergraph
hg = HypergraphDB()
# Add vertices
hg.add_v(1, {"name": "Alice", "age": 30, "city": "New York"})
hg.add_v(2, {"name": "Bob", "age": 24, "city": "Los Angeles"})
hg.add_v(3, {"name": "Charlie", "age": 28, "city": "Chicago"})
hg.add_v(4, {"name": "David", "age": 35, "city": "Miami"})
hg.add_v(5, {"name": "Eve", "age": 22, "city": "Seattle"})
hg.add_v(6, {"name": "Frank", "age": 29, "city": "Houston"})
hg.add_v(7, {"name": "Grace", "age": 31, "city": "Phoenix"})
hg.add_v(8, {"name": "Heidi", "age": 27, "city": "San Francisco"})
hg.add_v(9, {"name": "Ivan", "age": 23, "city": "Denver"})
hg.add_v(10, {"name": "Judy", "age": 26, "city": "Boston"})
# Add hyperedges
hg.add_e((1, 2, 3), {"type": "friendship", "duration": "5 years"})
hg.add_e((1, 4), {"type": "mentorship", "topic": "career advice"})
hg.add_e((2, 5, 6), {"type": "collaboration", "project": "AI Research"})
hg.add_e((4, 5, 7, 9), {"type": "team", "goal": "community service"})
hg.add_e((3, 8), {"type": "partnership", "status": "ongoing"})
hg.add_e((9, 10), {"type": "neighbors", "relationship": "friendly"})
hg.add_e((1, 2, 3, 7), {"type": "collaboration", "field": "music"})
hg.add_e((2, 6, 9), {"type": "classmates", "course": "Data Science"})
```
#### **2. Query Vertices and Hyperedges**
```python
# Get all vertices and hyperedges
print(hg.all_v) # Output: {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
print(hg.all_e) # Output: {(4, 5, 7, 9), (9, 10), (3, 8), (1, 2, 3), (2, 6, 9), (1, 4), (1, 2, 3, 7), (2, 5, 6)}
# Query a specific vertex
print(hg.v(1)) # Output: {'name': 'Alice', 'age': 30, 'city': 'New York'}
# Query a specific hyperedge
print(hg.e((1, 2, 3))) # Output: {'type': 'friendship', 'duration': '5 years'}
```
#### **3. Update and Remove Vertices/Hyperedges**
```python
# Update a vertex
hg.update_v(1, {"name": "Smith"})
print(hg.v(1)) # Output: {'name': 'Smith', 'age': 30, 'city': 'New York'}
# Remove a vertex
hg.remove_v(3)
print(hg.all_v) # Output: {1, 2, 4, 5, 6, 7, 8, 9, 10}
print(hg.all_e) # Output: {(4, 5, 7, 9), (9, 10), (1, 2, 7), (1, 2), (2, 6, 9), (1, 4), (2, 5, 6)}
# Remove a hyperedge
hg.remove_e((1, 4))
print(hg.all_e) # Output: {(4, 5, 7, 9), (9, 10), (1, 2, 7), (1, 2), (2, 6, 9), (2, 5, 6)}
```
#### **4. Calculate Degrees**
```python
# Get the degree of a vertex
print(hg.degree_v(1)) # Example Output: 2
# Get the degree of a hyperedge
print(hg.degree_e((2, 5, 6))) # Example Output: 3
```
#### **5. Neighbor Queries**
```python
# Get neighbors of a vertex
print(hg.nbr_v(1)) # Example Output: {2, 7}
hg.add_e((1, 4, 6), {"relation": "team"})
print(hg.nbr_v(1)) # Example Output: {2, 4, 6, 7}
# Get incident hyperedges of a vertex
print(hg.nbr_e_of_v(1)) # Example Output: {(1, 2, 7), (1, 2), (1, 4, 6)}
```
#### **6. Persistence (Save and Load)**
```python
# Save the hypergraph to a file
hg.save("my_hypergraph.hgdb")
# Load the hypergraph from a file
hg2 = HypergraphDB(storage_file="my_hypergraph.hgdb")
print(hg2.all_v) # Output: {1, 2, 4, 5, 6, 7, 8, 9, 10}
print(hg2.all_e) # Output: {(4, 5, 7, 9), (9, 10), (1, 2, 7), (1, 2), (2, 6, 9), (1, 4, 6), (2, 5, 6)}
```
---
## :memo: License
Hypergraph-DB is open-source and licensed under the [Apache License 2.0](LICENSE). Feel free to use, modify, and distribute it as per the license terms.
---
## :email: Contact
Hypergraph-DB is maintained by [iMoon-Lab](http://moon-lab.tech/), Tsinghua University. If you have any questions, please feel free to contact us via email: [Yifan Feng](mailto:evanfeng97@gmail.com).
Made with :heart: by Yifan Feng