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arXiv Machine Learning By Aleksandar Tom\v{c}i\'c, Milo\v{s} Savi\'c, Milo\v{s} Radovanovi\'c

Enhancing Distance-Based Graph Autoencoders with Structural Penalties for Dynamic Graph Embedding

Read the original on arXiv Machine Learning →

The paper introduces three distance‑based graph autoencoder variants that add structural penalties to the reconstruction loss. All models use a two‑layer Graph Convolutional Network encoder and a Euclidean‑distance decoder, with two node‑level regularizers: a hub penalty based on degree centrality and a penalty based on Natural Community Local Intrinsic Dimensionality (NC‑LID). Experiments on multiple dynamic graph datasets show that incorporating NC‑LID regularization consistently improves reconstruction performance compared to baselines without structural regularization and to the hub‑aware variant.

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