我们提出了一种新颖的建模框架，用于描述随时间演化的网络，能够捕捉网络特征在连续时间中更新时的长期依赖性。动态网络增长通过带标记点过程的条件强度进行函数参数化。这一表征使得能够灵活地联合建模更新时机及网络更新本身，且二者均依赖于完整的左连续样本路径。我们提出了一种路径相关的非线性带标记霍克斯过程作为建模此类数据的表达性强的平台；其动态标记空间嵌入了随时间演化的网络。我们证明了该模型的适定性，并建立了充分的稳定性条件，通过数值研究展示了模拟方法以及后续可行的基于似然的推断，最后通过一个会议参会者社交网络数据的应用实例说明了该方法的有效性。所提出的公式为连续时间复杂网络演化提供了灵活且有理论基础的统计推断框架。

We propose a novel modeling framework for time-evolving networks allowing for long-term dependence in network features that update in continuous time. Dynamic network growth is functionally parameterized via the conditional intensity of a marked point process. This characterization enables flexible, joint modeling of both update timing and the network updates themselves, dependent on the entire left-continuous sample path. We propose a path dependent nonlinear marked Hawkes process as an expressive platform for modeling such data; its dynamic mark space embeds the time-evolving network. We prove well-posedness and establish sufficient stability conditions, demonstrate simulation and subsequent feasible likelihood-based inference through numerical study, and illustrate the methodology with an application to conference attendee social network data. The proposed formulation provides a flexible and principled foundation for statistical inference on complex network evolution in continuous time.