Asynchronous Rumour Spreading in Social and Signed Topologies

TL;DR

This paper experimentally analyzes the asynchronous push & pull rumour spreading protocol on social networks, exploring how multiple parameters influence its efficiency and demonstrating potential improvements in dissemination speed in real-world systems.

Contribution

It is the first study to examine how various parameters jointly affect the protocol's performance using real social network data.

Findings

Parameter tuning can reduce spreading time by up to 99.69%.

Using memory and sophisticated neighbor selection improves efficiency.

The protocol's behavior varies significantly with network type and parameter settings.

Abstract

In this paper, we present an experimental analysis of the asynchronous push & pull rumour spreading protocol. This protocol is, to date, the best-performing rumour spreading protocol for simple, scalable, and robust information dissemination in distributed systems. We analyse the effect that multiple parameters have on the protocol's performance, such as using memory to avoid contacting the same neighbor twice in a row, varying the stopping criteria used by nodes to decide when to stop spreading the rumour, employing more sophisticated neighbor selection policies instead of the standard uniform random choice, and others. Prior work has focused on either providing theoretical upper bounds regarding the number of rounds needed to spread the rumour to all nodes, or, proposes improvements by adjusting isolated parameters. To our knowledge, our work is the first to study how multiple parameters affect system behaviour both in isolation and combination and under a wide range of values. Our analysis is based on experimental simulations using real-world social network datasets, thus complementing prior theoretical work to shed light on how the protocol behaves in practical, real-world systems. We also study the behaviour of the protocol on a special type of social graph, called signed networks (e.g., Slashdot and Epinions), whose links indicate stronger trust relationships. Finally, through our detailed analysis, we demonstrate how a few simple additions to the protocol can improve the total time required to inform 100% of the nodes by a maximum of 99.69% and an average of 82.37%.