Abstract
Graph Representation Learning aims to learn a rich and low-dimensional node embedding while preserving the graph properties. In this paper, we propose a novel Deep Attributed Graph Embedding (DAGE) that learns node representations based on both the topological structure and node attributes. DAGE a is able to capture, in a linear time and with a limited number of trainable parameters, the highly non-linear properties of attributed graphs. The proposed approach outperforms the current state-of-the-art approaches on node classification and node clustering tasks at a lower computational costs.
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Notes
- 1.
The source code of DAGE is available at https://github.com/MIND-Lab/DAGE.
- 2.
We employed the default parameters provided by the Scikit-learn Python library, training all the investigated models using the same SVM configuration scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html.
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Acknowledgments
This work has been supported by the project “ULTRA OPTYMAL - Urban Logistics and susTainable tRAnsportation: OPtimization under uncertainTY and MAchine Learning” funded by the MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) Bando 2020 - grant 20207C8T9M.
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Fersini, E., Mottadelli, S., Carbonera, M., Messina, E. (2022). Deep Attributed Graph Embeddings. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2022. Lecture Notes in Computer Science(), vol 13408. Springer, Cham. https://doi.org/10.1007/978-3-031-13448-7_15
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