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

TL;DR

This paper demonstrates that local network structure can identify influential spreaders in large social networks by leveraging nucleation behavior in spreading processes, enabling efficient influence measurement without full network knowledge.

Contribution

The study introduces a novel local-structure-based method to quantify influential nodes, bypassing the need for complete network information and enabling efficient seed selection.

Findings

Spreading exhibits nucleation behavior leading to global influence after a characteristic threshold.

Local influence measurement accurately predicts global influence independent of network size.

Proposed algorithm achieves near-optimal seed selection with constant time complexity.

Abstract

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.