https://github.com/jg-you/dyvider
Dynamic programming algorithms for exact linear clustering in networks.
https://github.com/jg-you/dyvider
clustering graph-algorithms machine-learning network-embedding
Last synced: 5 months ago
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Dynamic programming algorithms for exact linear clustering in networks.
- Host: GitHub
- URL: https://github.com/jg-you/dyvider
- Owner: jg-you
- License: mit
- Created: 2023-01-14T00:26:28.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-04T13:09:36.000Z (almost 3 years ago)
- Last Synced: 2025-09-30T15:24:22.851Z (9 months ago)
- Topics: clustering, graph-algorithms, machine-learning, network-embedding
- Language: Python
- Homepage:
- Size: 257 KB
- Stars: 16
- Watchers: 3
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# dyvider
**dyvider** is a small package implementing dynamic programming algorithms for exact linear clustering in networks.
Its algorithms process networks whose nodes have positions in one dimension, and return their optimal partition.
The theory and experiments exploring this code can be found in the paper [\"Exact and rapid linear clustering of networks with dynamic programming\"](https://arxiv.org/abs/2301.10403), by [Alice Patania](https://alpatania.github.io/), [Antoine Allard](https://antoineallard.github.io/) and [Jean-Gabriel Young](https://jg-you.github.io/).
## Dependencies
The only necessary dependency are [`networkx`](https://networkx.org/) and `numpy`.
## Installation
To install the latest version of dyvider, run the following command:
```sh
pip install dyvider
```
To install this package locally:
* Clone this repository
* Navigate to the folder on your local machine
* Run the following command:
```sh
pip install -e .
```
## Quick tour
The following minimal example first assigns scores to nodes with a one-dimensional spectral embedding and then retrieves an optimal linear clustering from this embedding using `dyvider`.
```python
import networkx as nx
import dyvider as dy
import numpy as np
# create a graph
g = nx.stochastic_block_model([10, 10], [[0.5, 0.05], [0.05, 0.5]], seed=42)
# generate a 1-d embedding with the leading eigenvector of the modularity matrix
eigenvals, eigvenvecs = np.linalg.eig(nx.linalg.adjacency_matrix(g).todense())
score = {v: float(eigvenvecs[v, 0]) for v in g.nodes()}
# set the node positions
nx.set_node_attributes(g, score, 'score')
# run dyvider
g = dy.utilities.preprocess(g)
objective_function = dy.objectives.Modularity()
solution, Q = dy.algorithms.run(g, objective_function)
print(solution)
```
The expected output is:
```python
>>> [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]; [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]]
```
Our [tutorial](tutorial.ipynb) goes into more detail and demonstrates all the API calls.
## Paper
If you use this code, please consider citing:
"[*Exact and rapid linear clustering of networks with dynamic programming*](https://arxiv.org/abs/2301.10403)"
[Alice Patania](https://alpatania.github.io/), [Antoine Allard](https://antoineallard.github.io/) and [Jean-Gabriel Young](https://jg-you.github.io/).
arXiv:2301.10403
## Author information
Code by [Jean-Gabriel Young](https://jg-you.github.io). Don't hesitate to get in touch at , or via the [issues](https://github.com/jg-you/dyvider/issues)!