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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 recon
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