Self-Organizing Map and social networks: Unfolding online social popularity

TL;DR

This paper applies the Kohonen self-organizing map to analyze and classify online social network behaviors of Myspace music artists, revealing patterns in popularity and influence.

Contribution

It introduces the use of SOM for clustering and interpreting complex social network data in the context of online music communities.

Findings

SOM effectively clusters social network profiles and friendship links.

The method reveals patterns linking audience engagement and musician influence.

Provides a robust classification of social network behaviors.

Abstract

The present study uses the Kohonen self organizing map (SOM) to represent the popularity patterns of Myspace music artists from their attributes on the platform and their position in the social network. The method is applied to cluster the profiles (the nodes of the social network) and the best friendship links (the edges). It shows that the SOM is an efficient tool to interpret the complex links between the audience and the influence of the musicians. It finally provides a robust classifier of the online social network behaviors.