CORDIS
EU research results

CORDIS

English EN
Modelling Trust-based Evolutionary Dynamics in Signed Social Networks

Modelling Trust-based Evolutionary Dynamics in Signed Social Networks

Objective

Users’ experience with real-world social systems (e.g., Epinions and eBay) witnesses the importance of Signed Social Networks (SSNs) that have wide practical and valuable applications in social media such as opinion guidance, personalized recommendation, and topic identification. However, the diversity of massive social interactions complicates the trust and distrust relations among users in SSNs. In particular, the complexity of distrust relations leads to significant challenges in detecting the trusted communities and capturing their evolutionary patterns.

This research aims to pioneer the innovative mechanisms for detecting the trusted communities and learning the evolutionary dynamics. To this end, we will explore the representation mechanism for SSNs by using the Formal Concept Analysis (FCA) and develop a FCA-based representation model. Next, the mechanisms and corresponding algorithms for detecting trusted communities and identifying their dynamic evolutions will be investigated. This research will provide both theoretical fundamentals and practical techniques for detection and dynamic evolution of trusted communities in SSNs. Moreover, this project can stimulate new research directions and the collaborative opportunities across multiple disciplines, such as social computing, soft computing and networking.

To broaden the fellow’s knowledge horizon, a series of research, training, and knowledge transfer activities are planned. The new knowledge and skills imparted in these activities will further promote the applicant’s research portfolio and significantly enhance his career prosperity. The research will also lay a solid foundation for the long-term and wide-range collaborations between the fellow and the host university, and eventually lead to more extensive and higher impact of research results, from which both EU and China will benefit.
Leaflet | Map data © OpenStreetMap contributors, Credit: EC-GISCO, © EuroGeographics for the administrative boundaries

Coordinator

THE UNIVERSITY OF EXETER

Address

The Queen'S Drive Northcote House
Ex4 4qj Exeter

United Kingdom

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 224 933,76

Project information

Grant agreement ID: 840922

Status

Grant agreement signed

  • Start date

    1 February 2020

  • End date

    31 January 2022

Funded under:

H2020-EU.1.3.2.

  • Overall budget:

    € 224 933,76

  • EU contribution

    € 224 933,76

Coordinated by:

THE UNIVERSITY OF EXETER

United Kingdom