Dynamic networks offer an insight of how relational systems evolve. However, modeling these networks efficiently remains a challenge, primarily due to computational constraints, especially as the number of observed events grows. This paper addresses this issue by introducing the Deep Relational Event Additive Model (DREAM) as a solution to the computational challenges presented by modeling non-linear effects in Relational Event Models (REMs). DREAM relies on Neural Additive Models to model non-linear effects, allowing each effect to be captured by an independent neural network. By strategically trading computational complexity for improved memory management and leveraging the computational capabilities of Graphic Processor Units (GPUs), DREAM efficiently captures complex non-linear relationships within data. This approach demonstrates the capability of DREAM in modeling dynamic networks and scaling to larger networks. Comparisons with traditional REM approaches showcase DREAM superior computational efficiency. The model potential is further demonstrated by an examination of the patent citation network, which contains nearly 8 million nodes and 100 million events.

翻译：动态网络揭示了关系系统的演化过程。然而，由于计算约束，特别是当观测事件数量不断增加时，如何高效地建模这些网络仍是一个挑战。本文通过引入深度关系事件可加性模型（DREAM），解决了关系事件模型（REM）中非线性效应建模面临的计算难题。DREAM利用神经可加性模型对非线性效应进行建模，使每个效应可由独立的神经网络单独捕获。通过策略性地以计算复杂度换取内存管理优化，并借助图形处理器（GPU）的计算能力，DREAM能够高效地捕捉数据中复杂的非线性关系。该模型展示了在动态网络建模及扩展至更大规模网络中的能力。与传统REM方法的对比表明，DREAM在计算效率上具有显著优势。通过分析包含近800万个节点和1亿条事件的专利引用网络，进一步验证了该模型的潜力。