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cs.LG2026
Enhancing Distance-Based Graph Autoencoders with Structural Penalties for Dynamic Graph Embedding
Aleksandar Tomčić, Miloš Savić, Miloš Radovanović
Graph autoencoders (GAEs) are widely used for learning representations of dynamic graphs. However, their optimisation objectives typically do not take structural heterogeneity acro…
cs.LG2024
Local Intrinsic Dimensionality for Dynamic Graph Embeddings
Dušica Knežević, Miloš Savić, Miloš Radovanović
The notion of local intrinsic dimensionality (LID) has important theoretical implications and practical applications in the fields of data mining and machine learning. Recent resea…