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Pytorch implementation of echo state networks for static graphs and discrete-time dynamic graphs.
Easiest way to get our library is via python package:
pip install graphesn
The library is quite straightforward to use:
from graphesn import StaticGraphReservoir, Readout, initializer
from torch_geometric.data import Data
data = Data(...)
reservoir = StaticGraphReservoir(num_layers=3, in_features=8, hidden_features=16)
reservoir.initialize_parameters(recurrent=initializer('uniform', rho=.9), input=initializer('uniform', scale=1))
embeddings = reservoir(data.edge_index, data.x)
readout = Readout(num_features=reservoir.out_features, num_targets=3)
readout.fit(data=(embeddings, data.y), regularization=1e-3)
predictions = readout(embeddings)
The library is contained in folder src/graphesn
:
reservoir.py
implementation of reservoirs for static and discrete-time dynamic graphs;matrix.py
random matrices generating functions;readout.py
implementation of a linear readout for large-scale ridge regression;data.py
classes to represent temporal and dynamic graphs;dataset.py
some dynamic graph datasets;util.py
general utilities.The examples
folder contains demos for our library on some common graph datasets.
This research software is provided as-is. We are working on this library in our spare time.
Code is released under the MIT license, see LICENSE
for details.
If you find a bug, please open an issue to report it, and we will do our best to solve it. For general or technical questions, please email us rather than opening an issue.
FAQs
Python implementation of Deep Graph Echo State Networks
We found that graphesn demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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