Abstract
In recent years, Graph Neural Networks (GNNs) have undergone rapid development and have become an essential tool for building representations of complex relational data. Large real-world graphs, characterised by sparsity in relations and features, necessitate dedicated tools that existing dense tensor-centred approaches cannot easily provide. To address this need, we introduce a GNNs module in Scikit-network, a Python package for graph analysis, leveraging sparse matrices for both graph structures and features. Our contribution enhances GNNs efficiency without requiring access to significant computational resources, unifies graph analysis algorithms and GNNs in the same framework, and prioritises user-friendliness.
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Notes
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We rely on the number of forks associated with each package on GitHub as a metric to gauge library usage. Please note that this metric provides only a partial view of actual project usage.
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Acknowledgements
The authors would like to thank Tiphaine Viard for the numerous discussions, as well as her insightful comments and suggestions about the paper.
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Delarue, S., Bonald, T. (2024). Sparse Graph Neural Networks with Scikit-Network. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1141. Springer, Cham. https://doi.org/10.1007/978-3-031-53468-3_2
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