用事件驱动模型实时计算网络节点重要性，比传统方法更准更快。
HawkesRank: Event-Driven Centrality for Real-Time Importance Ranking
- 基于多变量霍克斯过程建模内外部影响，动态衡量节点重要性。
- 在在线情绪传播中表现优于静态中心性指标，能实时追踪系统活动。
- 可分解内外影响，适合突发舆情、社交网络等实时分析场景。
在科学、经济和公共卫生领域，量化网络中的影响力至关重要。然而，现有中心性度量存在局限：依赖静态表示、启发式网络构建及纯内源性重要性定义，且与可观测行为缺乏语义关联。本文提出HawkesRank，一种基于多变量霍克斯点过程的动态框架，同时建模外生驱动（内在贡献）与内生放大（自激励与交叉激励），得到一个有理论基础、经验校准且可适应变化的重要性度量。经典中心性如Katz和PageRank可视为该框架的均场极限，揭示了其有效性与局限性。不同于静态平均，HawkesRank通过瞬时事件强度衡量重要性，支持预测、清晰的内外源分解，并能适应外部冲击。在模拟与在线交流平台情绪动态的实证分析中，结果表明其能紧密跟踪系统活动，持续优于静态中心性指标。
原文摘要 · Abstract (English)
Quantifying influence in networks is important across science, economics, and public health, yet widely used centrality measures remain limited: they rely on static representations, heuristic network constructions, and purely endogenous notions of importance, while offering little semantic connection to observable activity. We introduce HawkesRank, a dynamic framework grounded in multivariate Hawkes point processes that models exogenous drivers (intrinsic contributions) and endogenous amplification (self- and cross-excitation). This yields a principled, empirically calibrated, and adaptive importance measure. Classical indices such as Katz centrality and PageRank emerge as mean-field limits of the framework, clarifying both their validity and their limitations. Unlike static averages, HawkesRank measures importance through instantaneous event intensities, enabling prediction, transparent endo-exo decomposition, and adaptability to shocks. Using both simulations and empirical analysis of emotion dynamics in online communication platforms, we show that HawkesRank closely tracks system activity and consistently outperforms static centrality metrics.
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