Beyond Information Exchange: An Approach to Deploy Network Properties for Information Diffusion
This work addresses the need for efficient information diffusion in applications like pandemic prevention and marketing, but it appears incremental as it builds on existing models by focusing on neighborhood influence.
The paper tackles the problem of information diffusion in online social networks by proposing the Common Neighborhood Strategy (CNS) algorithm, which demonstrates that considering common neighborhoods improves diffusion speed and outspread compared to existing models, as shown in empirical evaluations on real-world datasets.
Information diffusion in Online Social Networks is a new and crucial problem in social network analysis field and requires significant research attention. Efficient diffusion of information are of critical importance in diverse situations such as; pandemic prevention, advertising, marketing etc. Although several mathematical models have been developed till date, but previous works lacked systematic analysis and exploration of the influence of neighborhood for information diffusion. In this paper, we have proposed Common Neighborhood Strategy (CNS) algorithm for information diffusion that demonstrates the role of common neighborhood in information propagation throughout the network. The performance of CNS algorithm is evaluated on several real-world datasets in terms of diffusion speed and diffusion outspread and compared with several widely used information diffusion models. Empirical results show CNS algorithm enables better information diffusion both in terms of diffusion speed and diffusion outspread.