TEAMS · Modelling Trust-based Evolutionary Dynamics in Signed Social Networks
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2020-02-01 → 2022-01-31
- EU contribution
- €224,934
- Participants
- 1
- Scheme
- MSCA-IF-EF-ST
Lines connect the coordinator with its partners.
Results in brief
Modelling Trust-based Evolutionary Dynamics in Signed Social Networks
The problem being addressed: This project focuses on detecting the trusted communities and capturing their evolutionary patterns over Signed Social Networks (SSNs) by using Formal Concept Analysis methodology. Importance: Signed Social Network, a special form of online social network, can effectively portray human social network and has a wide range of applications in personalized recommendation, attitude prediction, user characteristic analysis and clustering, spam site identification, etc. Based on the evidences of practical applications in SSNs, the research results achieved in the project can serve as theoretic tools and potential technical solutions for supporting the development of trusted communities identification in SSNs to address critical issues related to European and global industries such as social marketing, journalism, and political science, and hence bring benefits for the society. Overall objectives: This project aims to detect the trusted communities and capture their evolutionary patterns over SSNs. The specific objectives for the project “TEAMS” are listed as follows: (1) establish a unified representation model for a given signed social network; (2) establish the new model and develop innovative algorithms for detection of the trusted communities; (3) establish the evolutionary model and develop the innovative algorithms for the trusted communities; (4) systems evaluation, prototype system and demonstration for the trusted communities detection in SSNs.
Data: CORDIS, © European Union
Project 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.
Original text from CORDIS.
Participants
- THE UNIVERSITY OF EXETER · ExeterCoordinatorUnited Kingdom
Links
Data: CORDIS, © European Union