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https://github.com/h4kor/graph-force
Python library for embedding large graphs in 2D space, using force-directed layouts.
https://github.com/h4kor/graph-force
force-directed-graphs graph-algorithms python python3
Last synced: 5 days ago
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Python library for embedding large graphs in 2D space, using force-directed layouts.
- Host: GitHub
- URL: https://github.com/h4kor/graph-force
- Owner: H4kor
- Created: 2022-11-28T18:36:33.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2022-11-28T18:59:48.000Z (about 2 years ago)
- Last Synced: 2024-12-09T04:24:57.794Z (14 days ago)
- Topics: force-directed-graphs, graph-algorithms, python, python3
- Language: Rust
- Homepage: https://pypi.org/project/graph-force/
- Size: 33.2 KB
- Stars: 165
- Watchers: 10
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# Graph Force
A python/rust library for embedding graphs in 2D space, using force-directed layouts.
## Installation
```bash
pip install graph_force
```
## Usage
The first parameter defines the number of nodes in graph.
The second parameter is an iterable of edges, where each edge is a tuple of two integers representing the nodes it connects. Node ids start at 0.
```python
import graph_force
edges = [(0, 1), (1, 2), (2, 3), (3, 0)]
pos = graph_force.layout_from_edge_list(4, edges)
```
### Example with networkx
This library does not have a function to consume a networkx graph directly, but it is easy to convert it to an edge list.
```python
import networkx as nx
import graph_force
G = nx.grid_2d_graph(10, 10)
# we have to map the names to integers
# as graph_force only supports integers as node ids at the moment
edges = []
mapping = {n: i for i, n in enumerate(G.nodes)}
i = 0
for edge in G.edges:
edges.append((mapping[edge[0]], mapping[edge[1]]))
pos = graph_force.layout_from_edge_list(len(G.nodes), edges, iter=1000)
nx.draw(G, {n: pos[i] for n, i in mapping.items()}, node_size=2, width=0.1)
```
### Example with edge file
This methods can be used with large graphs, where the edge list does not fit into memory.
Format of the file:
- Little endian
- 4 bytes: number of nodes(int)
- 12 bytes: nodeA(int), nodeB(int), weight(float)
```python
import graph_force
import struct
with open("edges.bin", "rb") as f:
n = 10
f.write(struct.pack("i", n))
for x in range(n-1):
f.write(struct.pack("iif", x, x+1, 1))
pos = graph_force.layout_from_edge_file("edges.bin", iter=50)
```
### Options
`iter`, `threads` and `model`, `initial_pos` are optional parameters, supported by `layout_from_edge_list` and `layout_from_edge_file`.
```python
pos = graph_force.layout_from_edge_list(
number_of_nodes,
edges,
iter=500, # number of iterations, default 500
threads=0, # number of threads, default 0 (all available)
model="spring_model", # model to use, default "spring_model", other option is "networkx_model"
initial_pos=[(0.4, 0.7), (0.7, 0.2), ...], # initial positions, default None (random)
)
```
#### Available models
- `spring_model`: A simple spring model (my own implementation)
- `networkx_model`: Reimplementation of the [spring model from networkx](https://networkx.org/documentation/stable/reference/generated/networkx.drawing.layout.spring_layout.html)
## Contributing
- [Development](DEVELOPMENT.md)