Algorithm for Achieving Consensus Over Conflicting Rumors: Convergence Analysis and Applications

TL;DR

This paper analyzes how gossip algorithms can be used to reach consensus on one message among conflicting rumors in social networks, providing convergence analysis and practical applications.

Contribution

It introduces an algorithm for consensus over conflicting messages, with convergence analysis and applications in social network scenarios.

Findings

The algorithm successfully achieves consensus on one message.

Convergence properties are rigorously analyzed.

Applications include game theory and word-of-mouth marketing.

Abstract

Motivated by the large expansion in the study of social networks, this paper deals with the problem of multiple messages spreading over the same network using gossip algorithms. Given two messages distributed over some nodes of the graph, we first investigate the final distribution of the messages given an initial state. Then, an algorithm is presented to achieve consensus over one of the messages. Finally, a game theoretical application and an analogy with word-of-mouth marketing are outlined.