A python library for generating ABCD graphs.
pip
pip install abcd-graph
Project available at PyPI.
git clone https://github.com/AleksanderWWW/abcd-graph.git
# or - with ssh - git clone git@github.com:AleksanderWWW/abcd-graph.git
cd abcd-graph
pip install .
from abcd_graph import ABCDGraph, ABCDParams
params = ABCDParams()
graph = ABCDGraph(params, n=1000, logger=True).build()
params
: An instance of ABCDParams
class.n
: Number of nodes in the graph.logger
A boolean to enable or disable logging to the console. Default is False
- no logs are shown.callbacks
: A list of instances of Callback
class. Default is an empty list.The ABCDGraph
object with the generated graph.
ABCDParams
The ABCDParams
class is used to set the parameters for the graph generation.
Arguments:
Name | Type | Description | Default |
---|---|---|---|
gamma |
float |
Power-law parameter for degrees, between 2 and 3 | 2.5 |
delta |
int |
Min degree | 5 |
zeta |
float |
Parameter for max degree, between 0 and 1 | 0.5 |
beta |
float |
Power-law parameter for community sizes, between 1 and 2 | 1.5 |
s |
int |
Min community size | 20 |
tau |
float |
Parameter for max community size, between zeta and 1 | 0.8 |
xi |
float |
Noise parameter, between 0 and 1 | 0.25 |
Parameters are validated when the object is created. If any of the parameters are invalid, a ValueError
will be raised.
The ABCDGraph
object has two properties that can be used to access the communities and edges of the graph.
communities
- A list of ABCDCommunity
objects.edges
- A list of tuples representing the edges of the graph.Example:
from abcd_graph import ABCDGraph, ABCDParams
params = ABCDParams()
graph = ABCDGraph(params, n=1000, logger=True).build()
print(graph.communities)
print(graph.edges)
Communities have the following properties:
Exporting the graph to different formats is done via the exporter
property of the Graph
object.
Possible formats are:
Method | Description | Required packages | Installation command |
---|---|---|---|
to_networkx() |
Export the graph to a networkx.Graph object. |
networkx |
pip install abcd[networkx] |
to_igraph() |
Export the graph to an igraph.Graph object. |
igraph |
pip install abcd[igraph] |
adj_matrix |
Export the graph to a numpy.ndarray object representing the adjacency matrix. |
||
to_sparse_adjacency_matrix() |
Export the graph to a scipy.sparse.csr_matrix object representing the adjacency matrix. |
scipy |
pip install abcd[scipy] |
Example:
from abcd_graph import ABCDGraph, ABCDParams
params = ABCDParams()
graph = ABCDGraph(params, n=1000, logger=True).build()
graph_networkx = graph.exporter.to_networkx()
Callbacks are used to handle diagnostics and visualization of the graph generation process. They are instances of the ABCDCallback
class.
Out of the box, the library provides three callbacks:
StatsCollector
- Collects statistics about the graph generation process.PropertyCollector
- Collects properties of the graph.Visualizer
- Visualizes the graph generation process.Example:
from abcd_graph import ABCDGraph, ABCDParams
from abcd_graph.callbacks import StatsCollector, Visualizer, PropertyCollector
stats = StatsCollector()
vis = Visualizer()
props = PropertyCollector()
params = ABCDParams()
g = ABCDGraph(params, n=1000, logger=True, callbacks=[stats, vis, props]).build()
print(stats.statistics)
print(props.xi_matrix)
vis.draw_community_cdf()
To build a docker image containing the library, run:
docker build -t abcd-graph .
To run the image, use:
docker run -it abcd-graph /bin/bash
This will give you a terminal inside a container with the library installed.
Available are also installation commands for the additional packages:
docker build -t abcd-test --build-arg INSTALL_TYPE=igraph .
Possible values for INSTALL_TYPE
are dev
, matplotlib
, networkx
, igraph
, scipy
, all
and extended
.
Value | Packages installed |
---|---|
dev |
pytest , pre-commit , pytest-cov |
matplotlib |
matplotlib |
networkx |
networkx |
igraph |
igraph |
scipy |
scipy |
all |
networkx , igraph , scipy , pytest , pre-commit , pytest-cov , matplotlib |
extended |
scipy , matplotlib |
[!NOTE] Combinations of the above values are also possible, e.g.
igraph,networkx
.[!WARNING] If you choose and option outside the available ones, the installation will still succeed, but only the base package will be installed.
The library comes with a set of examples that show how to use the library in different scenarios.
You can find them in the examples
directory in the format of Jupyter Notebooks.