The ability to anticipate technical expertise and capability evolution trends globally is essential for national and global security, especially in safety-critical domains like nuclear nonproliferation (NN) and rapidly emerging fields like artificial intelligence (AI). In this work, we extend traditional statistical relational learning approaches (e.g., link prediction in collaboration networks) and formulate a problem of anticipating technical expertise and capability evolution using dynamic heterogeneous graph representations. We develop novel capabilities to forecast collaboration patterns, authorship behavior, and technical capability evolution at different granularities (e.g., scientist and institution levels) in two distinct research fields. 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 that our DGT models predict collaboration, partnership, and expertise patterns with 0.26, 0.73, and 0.53 mean reciprocal rank values for AI and 0.48, 0.93, and 0.22 for NN domains. DGT model performance exceeds the best-performing static graph baseline models by 30-80% across AI and NN domains. Our findings demonstrate that DGT models boost inductive task performance, when previously unseen nodes appear in the test data, for the domains with emerging collaboration patterns (e.g., AI). Specifically, models accurately predict which established scientists will collaborate with early career scientists and vice-versa in the AI domain.

翻译：在全球范围内预见技术专长与能力演进趋势，对于国家和全球安全至关重要，尤其涉及核不扩散（NN）等关键安全领域及人工智能（AI）等快速新兴领域。本研究拓展了传统统计关系学习方法（如协作网络中的链接预测），通过动态异构图表示构建了一个预测技术专长与能力演进的框架。我们开发了新型能力，用于在两个不同研究领域的不同粒度（如科学家与机构层面）预测协作模式、作者行为及技术能力演进。我们实现了一种动态图变换器（DGT）神经网络架构，该架构通过（a）预测异构（而非同质）节点与边，以及（b）同时依赖离散时间与连续时间输入，推动了当前最先进的图神经网络模型发展。实验表明，DGT模型在AI领域对协作、合作及专长模式的预测平均倒数排名值分别为0.26、0.73和0.53，在NN领域分别为0.48、0.93和0.22。在AI与NN领域，DGT模型性能较最优静态图基线模型提升30-80%。研究结果证明，对于存在新兴协作模式（如AI领域）的领域，当测试数据中出现未见过的节点时，DGT模型可提升归纳任务性能。具体而言，模型能准确预测AI领域中资深科学家与早期职业科学家之间的协作关系方向。