Enhancing Distance-Based Graph Autoencoders with Structural Penalties for Dynamic Graph Embedding
Graph autoencoders (GAEs) are widely used for learning representations of dynamic graphs. However, their optimisation objectives typically do not take structural heterogeneity across nodes into account. We propose three distance-based GAE variants that incorporate structural penalties into the reconstruction loss. All variants share a two-layer Graph Convolutional Network encoder and a Euclidean-distance decoder trained with distance-based reconstruction objectives. We extend sparsity-corrected loss with two node-level regularization terms: (i) a hub penalty based on degree centrality, and (ii) a penalty based on Natural Community Local Intrinsic Dimensionality (NC-LID). The paper is moti...
| Authors | Aleksandar Tomčić, Miloš Savić, Miloš Radovanović |
|---|---|
| Primary category | cs.LG |
| Published | 19 August 2026 |
| Last updated at source | 6 October 2026 |
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