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

arXiv: https://arxiv.org/abs/2603.11472 · HTML: https://mlsanity.com/paper/2603.11472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 60 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity](https://mlsanity.com/paper/1506.02594/) (2015)
- [PageRank beyond the Web](https://mlsanity.com/paper/1407.5107/) (2014)
- [Vital nodes identification in complex networks](https://mlsanity.com/paper/1607.01134/) (2016)
- [Machine Learning in Network Centrality Measures: Tutorial and Outlook](https://mlsanity.com/paper/1810.11760/) (2018)
- [Ranking in evolving complex networks](https://mlsanity.com/paper/1704.08027/) (2017)
- [Finding events in temporal networks: Segmentation meets densest-subgraph discovery](https://mlsanity.com/paper/1808.09317/) (2018)
- [Recommending investors for new startups by integrating network diffusion and investors' domain preference](https://mlsanity.com/paper/1912.02962/) (2019)
- [Network-based ranking in social systems: three challenges](https://mlsanity.com/paper/2005.14564/) (2020)
- [Quantifying the Influence of User Behaviors on the Dissemination of Fake News on Twitter with Multivariate Hawkes Processes](https://mlsanity.com/paper/2308.13927/) (2023)
- [Social feedback amplifies emotional language in online video live chats](https://mlsanity.com/paper/2408.05700/) (2024)
- [Data-Driven Law Firm Rankings to Reduce Information Asymmetry in Legal Disputes](https://mlsanity.com/paper/2408.16863/) (2024)

## Abstract (arXiv)

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.