HawkesRank: Event-Driven Centrality for Real-Time Importance Ranking
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 limi...
| Authors | Didier Sornette, Yishan Luo, Sandro Claudio Lera |
|---|---|
| Primary category | cs.SI |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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