Multilayer network decoding versatility and trust

TL;DR

This paper uses multilayer network analysis on Bollywood social data to reveal actor versatility, motif importance, and trust indicators, providing insights into complex social dynamics and network properties.

Contribution

It introduces a multilayer network framework to analyze actor versatility, motifs, and trust in a large-scale social system, revealing new structural insights.

Findings

Lead actors exhibit intrinsic versatility.

Higher-order motifs influence success and relationships.

Multilayer correlations indicate trust and importance of weak ties.

Abstract

In the recent years, the multilayer networks have increasingly been realized as a more realistic framework to understand emergent physical phenomena in complex real world systems. We analyze a massive time-varying social data drawn from the largest film industry of the world under multilayer network framework. The framework enables us to evaluate the versatility of actors, which turns out to be an intrinsic property of lead actors. Versatility in dimers suggests that working with different types of nodes are more beneficial than with similar ones. However, the triangles yield a different relation between type of co-actor and the success of lead nodes indicating the importance of higher order motifs in understanding the properties of the underlying system. Furthermore, despite the degree-degree correlations of entire networks being neutral, multilayering picks up different values of correlation indicating positive connotations like trust, in the recent years. Analysis of weak ties of the industry uncovers nodes from lower degree regime being important in linking Bollywood clusters. The framework and the tools used herein may be used for unraveling the complexity of other real world systems.