# Local structure can identify and quantify influential global spreaders in large scale social networks

arXiv: https://arxiv.org/abs/1509.03484 · HTML: https://mlsanity.com/paper/1509.03484/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2015.

## Built on by (most cited first, 3 of 3)

- [Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications](https://mlsanity.com/paper/2303.16694/) (2023)
- [Heterogeneous Influence Maximization in User Recommendation](https://mlsanity.com/paper/2508.13517/) (2025)
- [Deep learning framework for predicting stochastic take-off and die-out of early spreading](https://mlsanity.com/paper/2510.04574/) (2025)

## Abstract (arXiv)

Measuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Employing percolation theory, we show that the spreading process displays a nucleation behavior: once a piece of information spread from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure, otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of entire network, any nodes' global influence can be accurately measured using this characteristic number, which is independent of the network size. This motivates an efficient algorithm with constant time complexity on the long standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.