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.
Fersini, E., Mottadelli, S., Carbonera, M., Messina, V. (2022). Deep Attributed Graph Embeddings. In Modeling Decisions for Artificial Intelligence 19th International Conference, MDAI 2022, Sant Cugat, Spain, August 30 – September 2, 2022, Proceedings (pp.181-192). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-13448-7_15].
Deep Attributed Graph Embeddings
Fersini E.
;Mottadelli S.;Carbonera M.;Messina V.
2022
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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