3 papers
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.SI2025
Dynamic Graph Embedding Through Hub-aware Random Walks
Aleksandar Tomčić, Miloš Savić, Dušan Simić +1
The role of high-degree nodes, or hubs, in shaping graph dynamics and structure is well-recognized in network science, yet their influence remains underexplored in the context of d…
cs.LG2022
Hub-aware Random Walk Graph Embedding Methods for Classification
Aleksandar Tomčić, Miloš Savić, Miloš Radovanović
In the last two decades we are witnessing a huge increase of valuable big data structured in the form of graphs or networks. To apply traditional machine learning and data analytic…