An effective scheme to address influence maximization for opinion formation in social networks

Abstract

Influence maximization for opinion formation (IMOF) is an important problem in social networks, which aims to select most influential nodes and obtains the maximal propagation of the most ideal opinions. The existing studies on the IMOF primarily concentrate on the effective selection scheme of the most influential nodes (ie, seed nodes) and the improved opinion formation models. However, there is little work describing and defining the IMOF mathematically. In this paper, we formulate the IMOF problem mathematically and propose the weighted cooperation model to compute the opinions of network nodes. To determine the most influential nodes, an effective scheme (including the score of potential nodes (SPNs) and elimination of overlapping influence (EOI)) is proposed. By using the reverse propagation process of public opinion and dynamically adjusting the influence score of each node, SPN determines the sorting of network node within the finite iterations. Moreover, to better determine the initial seed nodes, the EOI is adopted to deal with the similarity and overlapping influence among these nodes with high influence score. Finally, to evaluate the performance of SPN, the number of positive nodes with the change of seed nodes and the times of iteration by SPN is compared with three mainstream algorithms in social network data sets. Simulation results show that the proposed scheme obtains superior number of positive nodes than the comparison algorithms.

The Frequency of Using Websites and Social Media by Various Age Groups to Form Opinions about Scientific Topics: Findings from the European Context

“…People's beliefs and perceptions have a great influence on the economy, politics, and culture [1]. However, evidence shows that people's opinions are at odds with the broad scientific consensus.…”

Opinion Dynamics over a Finite Set in Cooperative Multi-robot Systems: An Asynchronous Gossip-Based Consensus Approach

“…There are many situations in robotics where a team needs to decide over a finite set of options. For example, for a distributed group decision making on resource allocation under conditions of uncertainty that disallow formal optimization (Friedkin et al, 2019); an effective scheme to address influence maximization, which aims to select most influential nodes and obtains the maximal propagation of the most ideal information (He et al, 2019); a distributed sensor selection architecture for a class of networked systems, dependent on the quality of the measurements to select the most suitable sensors (Tedesco et al, 2018); distributed strategy selection based on the individual opinions and the relative credibility of each agent (Ekenberg et al, 1994); and in many situations, like multirobot formation, it is necessary to choose a leader.…”