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Local structure can identify and quantify influential global spreaders in large scale social networks
Yanqing Hu1, Shenggong Ji2, Yuliang Jin3
1School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China; huyanq@mail.sysu.edu.cn hes@bu.edu.
Summary
Node influence in big-data online social networks can be measured without knowing the entire network. A characteristic number determines if information spreads globally or locally, enabling efficient seed spreader selection.
Area of Science:
- Network Science
- Computational Social Science
- Information Spreading Dynamics
Background:
- Measuring node influence in large online social networks is crucial for applications like viral marketing.
- Traditionally, influence measurement was believed to require complete network knowledge.
- Viral spreading dynamics present complex challenges in big data environments.
Purpose of the Study:
- To develop a method for accurately measuring global node influence without full network visibility.
- To identify a characteristic number that predicts the outcome of information spreading.
- To devise an efficient algorithm for selecting optimal seed spreaders.
Main Methods:
- Application of percolation theory to model information diffusion.
- Analysis of nucleation behavior in spreading processes.
- Development of a constant time complexity algorithm for seed selection.
Main Results:
- Information spreading exhibits nucleation behavior, reaching a point of no return after a characteristic number of nodes are influenced.
- Global influence can be accurately measured using this characteristic number, independent of network size.
- The proposed algorithm for seed spreader selection achieves near-optimal performance.
Conclusions:
- Node influence measurement and seed selection in big-data social networks can be efficiently performed without complete network data.
- Percolation theory provides a novel framework for understanding and predicting viral spreading.
- The findings offer practical implications for optimizing marketing and adoption strategies.
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