Beyond Information Exchange: An Approach to Deploy Network Properties for Information Diffusion

TL;DR

This paper introduces the CNS algorithm, which leverages common neighborhood properties to enhance information diffusion in social networks, outperforming existing models in speed and spread.

Contribution

The paper proposes a novel CNS algorithm that systematically explores neighborhood influence, advancing understanding of network properties in information diffusion.

Findings

CNS outperforms existing models in diffusion speed.

CNS achieves greater diffusion outspread.

Empirical results validate the effectiveness of CNS.

Abstract

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.