Multilayer weighted social network model

TL;DR

This paper introduces a multilayer weighted social network model that captures overlapping communities and maintains the Granovetterian structure by incorporating geographic constraints, addressing the limitations of single-layer models.

Contribution

It proposes a novel geographic multilayer WSN model that preserves community overlaps and the Granovetterian structure through interlayer correlations based on geographic proximity.

Findings

Interlayer correlation is crucial for maintaining topology-link weight relationships.

Geographic constraints enhance community overlaps in multilayer networks.

The model successfully combines community overlap with the Granovetterian structure.

Abstract

Recent empirical studies using large-scale data sets have validated the Granovetter hypothesis on the structure of the society in that there are strongly wired communities connected by weak ties. However, as interaction between individuals takes place in diverse contexts, these communities turn out to be overlapping. This implies that the society has a multilayered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, a sufficient amount of interlayer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multilayer WSN model, where the indirect interlayer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved.