Launchers and Targets in Social Networks

TL;DR

This paper introduces two new topological indices to identify key influencers and targets in social networks, aiding in efficient message propagation analysis and combating malicious information spread.

Contribution

The paper proposes novel indices for analyzing influence and receptivity in social networks, enhancing influencer selection and target identification methods.

Findings

Indices effectively distinguish influential launchers and targets.

Application to real-world networks demonstrates practical utility.

Potential to improve viral marketing and misinformation control.

Abstract

Influence propagation in social networks is a subject of growing interest. A relevant issue in those networks involves the identification of key influencers. These players have an important role on viral marketing strategies and message propagation, including political propaganda and fake news. In effect, an important way to fight malicious usage on social networks is to understand their properties, their structure and the way messages propagate. This paper proposes two new indices for analysing message propagation in social networks, based on the network topological nature and the power of the message. The first index involves the strength of each node as a launcher of the message, dividing the nodes into launchers and non-launchers. The second index addresses the potential of each member as a receiver (target) of the message, dividing the nodes into targets and non-targets. Launcher individuals should indicate strong influencers and target individuals should identify the best target consumers. These indices can assist other known metrics when used to select efficient influencers in a social network. For instance, instead of choosing a strong and probably expensive member according to its degree in the network (number of followers), we may previously select those belonging to the launchers group and look for the lowest degree members, which are probably cheaper but still guarantying almost the same influence effectiveness as the largest degree members. On a different direction, using the second index, the strong target members should characterize relevant consumers of information in the network, which may include fake news' regular collectors. We discuss these indices using small-world randomly generated graphs and a number of real-world social networks available in known datasets repositories.