Globoko relacijsko učenje nad bibliografskimi podatki

Research–information systems such as SICRIS record publications, collaborations, and projects over long periods, and form relational, time–stamped data that are naturally represented as heterogeneous temporal graphs. In this thesis, a leakage–safe forecasting pipeline is developed for mentorship outcomes by casting the SICRIS schema into a heterogeneous graph and evaluating models under seed–time splits with a five–year prediction horizon. The core of the model consists of: a Heterogeneous Graph Transformer, pre–norm residual layers, Jumping Knowledge aggregation, and stochastic graph regularization (DropEdge/Dropout). We compare against a strong tabular baseline, XGBoost. Tasks are defined at researcher–seed times and cover both binary outcomes (whether a doctoral or master mentorship occurs) and count outcomes (how many such mentorships occur) within the forward window. Empirical results on the SICRIS–derived graph demonstrate that the heterogeneous, time–aware model provides consistent gains over the tabular baseline. The analysis indicates that typed attention with relative temporal encoding and Jumping Knowledge aggregation improves the use of multi–hop, multi–relation evidence, and that stochastic graph regularization helps stabilize deeper stacks.