# Fast influencers in complex networks

## TL;DR

This paper distinguishes between nodes that quickly initiate large-scale spreading in networks and those influential in the long run, introducing a local centrality metric called social capital that effectively identifies fast influencers.

## Contribution

The study reveals a fundamental difference between fast and late-time influencers and proposes a simple local centrality measure to identify fast influencers efficiently.

## Key findings

- Local properties can identify fast influencers effectively.
- Social capital centrality outperforms other metrics in early-time influence detection.
- Local metrics are also competitive for late-time influence identification.

## Abstract

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.