# Fast influencers in complex networks

arXiv: https://arxiv.org/abs/1903.06367 · HTML: https://mlsanity.com/paper/1903.06367/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [Predicting Critical Nodes in Temporal Networks by Dynamic Graph Convolutional Networks](https://mlsanity.com/paper/2106.10419/) (2021)
- [Multi-perspective Memory Enhanced Network for Identifying Key Nodes in Social Networks](https://mlsanity.com/paper/2403.15235/) (2024)

## Abstract (arXiv)

Influential nodes in complex networks are typically defined as those nodes that maximize the asymptotic reach of a spreading process of interest. However, for practical applications such as viral marketing and online information spreading, one is often interested in maximizing the reach of the process in a short amount of time. The traditional definition of influencers in network-related studies from diverse research fields narrows down the focus to the late-time state of the spreading processes, leaving the following question unsolved: which nodes are able to initiate large-scale spreading processes, in a limited amount of time? Here, we find that there is a fundamental difference between the nodes -- which we call "fast influencers" -- that initiate the largest-reach processes in a short amount of time, and the traditional, "late-time" influencers. Stimulated by this observation, we provide an extensive benchmarking of centrality metrics with respect to their ability to identify both the fast and late-time influencers. We find that local network properties can be used to uncover the fast influencers. In particular, a parsimonious, local centrality metric (which we call social capital) achieves optimal or nearly-optimal performance in the fast influencer identification for all the analyzed empirical networks. Local metrics tend to be also competitive in the traditional, late-time influencer identification task.