Interests Diffusion on a Semantic Multiplex

TL;DR

This paper introduces the concept of a Semantic Multiplex to analyze how individual interests propagate across different social network layers, combining complexity science and semantic web techniques.

Contribution

It presents a hybrid model integrating multiplex social network structures with semantic layers to study interest diffusion, a novel approach in this domain.

Findings

Identified common features in interest propagation across different scientific communities.

Highlighted specific differences in interest diffusion patterns between the two studied networks.

Demonstrated the effectiveness of the Semantic Multiplex model in capturing complex social phenomena.

Abstract

Exploiting the information about members of a Social Network (SN) represents one of the most attractive and dwelling subjects for both academic and applied scientists. The community of Complexity Science and especially those researchers working on multiplex social systems are devoting increasing efforts to outline general laws, models, and theories, to the purpose of predicting emergent phenomena in SN's (e.g. success of a product). On the other side the semantic web community aims at engineering a new generation of advanced services tailored to specific people needs. This implies defining constructs, models and methods for handling the semantic layer of SNs. We combined models and techniques from both the former fields to provide a hybrid approach to understand a basic (yet complex) phenomenon: the propagation of individual interests along the social networks. Since information may move along different social networks, one should take into account a multiplex structure. Therefore we introduced the notion of "Semantic Multiplex". In this paper we analyse two different semantic social networks represented by authors publishing in the Computer Science and those in the American Physical Society Journals. The comparison allows to outline common and specific features