Enhancing Graph Data Embeddings with Machine Learning: The Deep Manifold Graph Auto-Encoder (DMVGAE/DMGAE) Approach

Manifold learning, rooted in the manifold assumption, reveals low-dimensional structures within input data, positing that the data exists on a low-dimensional manifold within a high-dimensional ambient space. Deep Manifold Learning (DML), facilitated by deep neural networks, extends to graph data applications. For instance, MGAE leverages auto-encoders in the graph domain to embed node features and […]

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