Deep Attributed Graph Embeddings

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.