Gossip on Weighted Networks

TL;DR

This paper explores how weighted social networks influence gossip spreading, revealing that gossip propagation patterns can distinguish human interaction networks from other network types.

Contribution

It extends existing gossip models to weighted networks, analyzing real-world human interaction data and comparing it with synthetic networks.

Findings

Gossip spreads differently in weighted networks based on friendship strength.

Gossip propagation can differentiate human social networks from artificial ones.

Real human networks show distinct gossip patterns compared to ER, BA, WS models.

Abstract

We investigate how suitable a weighted network is for gossip spreading. The proposed model is based on the gossip spreading model introduced by Lind et.al. on unweighted networks. Weight represents "friendship." Potential spreader prefers not to spread if the victim of gossip is a "close friend". Gossip spreading is related to the triangles and cascades of triangles. It gives more insight about the structure of a network. We analyze gossip spreading on real weighted networks of human interactions. 6 co-occurrence and 7 social pattern networks are investigated. Gossip propagation is found to be a good parameter to distinguish co-occurrence and social pattern networks. As a comparison some miscellaneous networks and computer generated networks based on ER, BA, WS models are also investigated. They are found to be quite different than the human interaction networks.