PyMulSim is a Python library for pairwise node similarity between multilayer networks. Embeddings are computed by using a model based on Graph Neural Network (GNN).
It was also released on PyPi: https://pypi.org/project/pymulsim
To get started with PyMulSim, you have to install it via pip:
pip install pymulsim
To begin using PyMulSim, follow these steps:
- Import the PyMulSim class in your Python script:
from pymulsim import PyMulSim
- Create an instance of the PyMulSim class:
pymulsim = PyMulSim()
- Use the available methods for computing node similarities between two given multilayer networks.
PyMulSim allows computing node similarities between two given multilayer networks by using both data from files and NetworkX objects.
You can choose the method that best suits your data and use case.
To compute node similarities from data stored in files, prepare two sets of graph data files: one for the source multilayer network, and one for the target multilayer network.
Note that we made available a (toy) dataset (folder: 'dataset')and a related example code (file: 'example.py'), for demonstration purpose only.
node_similarities = pymulsim.compute_similarities_from_file( source_path:(str), target_path:(str), layer_ids:(list), interlayer_ids:(list) )
Alternatively, you can compute node similarities by using two multilayer networks modelled as a set of NetworkX (nx) objects.
similarities = pymulsim.compute_similarities_from_nx( nx_set1:(list:nx.Graph), nx_set2(list:nx.Graph), layer_ids:(list), interlayer_ids:(list) )
MIT License - Copyright (c) 2023 Pietro Cinaglia
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