A New BAT and PageRank algorithm for Propagation Probability in Social Networks

TL;DR

This paper introduces a novel algorithm combining BAT and PageRank techniques to accurately evaluate propagation probability in social networks, aiming to optimize information dissemination with minimal resources.

Contribution

It presents a new algorithm integrating BAT, PageRank, and scale-free network models to improve propagation probability estimation in social networks.

Findings

The proposed algorithm effectively increases propagation efficiency.

Simulation results show improved accuracy in propagation probability estimation.

The method reduces resource investment for maximum information spread.

Abstract

Social networks have increasingly become important and popular in modern times. Moreover, the influence of social networks plays a vital role in various organizations including government organizations, academic research or corporate organizations. Therefore, how to strategize the optimal propagation strategy in social networks has also become more important. By increasing the precision of evaluating the propagation probability of social network, it can indirectly influence the investment of cost, manpower and time for information propagation to achieve the best return. This study proposes a new algorithm, which includes a scale-free network, Barabasi-Albert model, Binary-Addition-Tree (BAT) algorithm, PageRank algorithm, personalized PageRank algorithm and a new BAT algorithm, to calculate the propagation probability in social networks. The results obtained after implementing the simulation experiment of social network models show the studied model and the proposed algorithm provide an effective method to increase the efficiency of information propagation in social networks. In this way, the maximum propagation efficiency is achieved with the minimum investment.