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The ability to anticipate technical expertise and capability evolution trendsglobally is essential for national and global security, especially insafety-critical domains like nuclear nonproliferation (NN) and rapidly emergingfields like artificial intelligence (AI). In this work, we extend traditionalstatistical relational learning approaches (e.g., link prediction incollaboration networks) and formulate a problem of anticipating technicalexpertise and capability evolution using dynamic heterogeneous graphrepresentations. We develop novel capabilities to forecast collaborationpatterns, authorship behavior, and technical capability evolution at differentgranularities (e.g., scientist and institution levels) in two distinct researchfields. We implement a dynamic graph transformer (DGT) neural architecture,which pushes the state-of-the-art graph neural network models by (a)forecasting heterogeneous (rather than homogeneous) nodes and edges, and (b)relying on both discrete -- and continuous -- time inputs. We demonstrate thatour DGT models predict collaboration, partnership, and expertise patterns with0.26, 0.73, and 0.53 mean reciprocal rank values for AI and 0.48, 0.93, and0.22 for NN domains. DGT model performance exceeds the best-performing staticgraph baseline models by 30-80% across AI and NN domains. Our findingsdemonstrate that DGT models boost inductive task performance, when previouslyunseen nodes appear in the test data, for the domains with emergingcollaboration patterns (e.g., AI). Specifically, models accurately predictwhich established scientists will collaborate with early career scientists andvice-versa in the AI domain.

Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers