HawkesRank: Event-Driven Centrality for Real-Time Importance Ranking

Didier Sornette, Yishan Luo and Sandro Claudio Lera
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Didier Sornette: Risks-X, Southern University of Science and Technology (SUSTech); Swiss Finance Institute
Yishan Luo: Southern University of Science and Technology
Sandro Claudio Lera: Southern University of Science and Technology

No 26-28, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: 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.

Keywords: dynamic centrality; Hawkes processes; network ranking; event-driven models; information diffusion (search for similar items in EconPapers)
JEL-codes: C32 C55 C63 D83 D85 L86 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2026-03
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