Effects of Network Communities and Topology Changes in Message-Passing Computation of Harmonic Influence in Social Networks

TL;DR

This paper investigates how community structures and dynamic topology changes affect the performance of a distributed message-passing algorithm used to measure node importance in social networks, through experiments on real and synthetic networks.

Contribution

It provides empirical insights into the effects of communities and topology changes on the harmonic influence algorithm's accuracy and adaptability in social network analysis.

Findings

Communities can cause overestimation of local leaders' importance.

The algorithm adapts smoothly to topology changes.

Communities may introduce artifacts in importance approximation.

Abstract

The harmonic influence is a measure of the importance of nodes in social networks, which can be approximately computed by a distributed message-passing algorithm. In this extended abstract we look at two open questions about this algorithm. How does it perform on real social networks, which have complex topologies structured in communities? How does it perform when the network topology changes while the algorithm is running? We answer these two questions by numerical experiments on a Facebook ego network and on synthetic networks, respectively. We find out that communities can introduce artefacts in the final approximation and cause the algorithm to overestimate the importance of "local leaders" within communities. We also observe that the algorithm is able to adapt smoothly to changes in the topology.