WP1: Representation of Signed Social Networks
It regarded the vertex V in the signed social network as the set of objects and attributes with respect to the formal context in FCA, which is formalized as FC(G) = (V, V, I), I represents the positive and
negative relationship between nodes. And these relationships will be stored in an extended adjacency matrix M.
WP2-Investigation on Detection of Trusted Communities
In this WP, two efficient concept lattice generation algorithms Add-FCA and Dec-FCA, are developed for processing the incremental data. With these efficient algorithms, communities detection in social networks are investigated. These research results have been published in IEEE TNSE.
Y. Yang, F. Hao*, et.al: “Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach", IEEE Transactions on Network Science and Engineering, 2021.
Specifically, this project proved the equivalence between maximal clique and equiconcept. Further, a concept interestingness learning framework for identifying key topological structures from social networks is developed. These research results have been published in IEEE TNSE and GPC.
J. Gao, F. Hao*, et.al: “Learning Concept Interestingness for Identifying Key Structures from Social Networks", IEEE Transactions on Network Science and Engineering, Vol.8 No.4 pp.3220-3232 2021.
J. Gao, F. Hao*, et.al: “Concept Stability Based Isolated Maximal Cliques Detection in Dynamic Social Networks", The 15th International Conference on Green, Pervasive and Cloud Computing, pp.131–144, 2020.
In addition, this project utilized the three-way concept analysis methodology and stability of three-way concepts to address the structural issues in social networks.
F. Hao*, et.al: “Incremental Construction of Three-way Concept Lattice for Knowledge Discovery in Social Networks", Information Sciences, Vol.578 pp.257-280 2021.
F. Hao*, et.al: “Stability of Threeway Concepts and Its Application to Natural Language Generation", Pattern Recognition Letters, Vol.149 pp.51-58 2021.
Moreover, social media polarization problem is becoming increasingly serious, in order to model the multipolarization structure existing in the real signed networks, this project also developed a new cohesive subgraph model, called maximal multipolarized cliques based on the structure clustering theory. This research result has been published in ACM SIGIR 2021.
J. Gao, F. Hao*, et.al: “Maximal Multipolarized Cliques Search in Signed Networks", The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.2227–2231, 2021.
Considering the fuzzy property of social relationships, this project formulated a novel model of the skyline (λ, k)-cliques over a fuzzy attributed social network
and developed a Formal Concept Analysis (FCA) based skyline (λ, k)-cliques identification algorithm.
F. Hao*, et.al: “Skyline (λ, k)-cliques Identification from Fuzzy Attributed Social Networks", IEEE Transactions on Computational Social Systems, 2021.
WP3-Investigation on Evolutionary Dynamics of Trusted Communities
This WP mainly explores the evolution of maximal cliques in social networks byy observing different evolutionary patterns of maximal cliques including (1)unchanged maximal cliques; (2)changed maximal cliques; (3)added maximal cliques; (4) vanished maximal cliques, in social networks. To address it, this project described the evolution of social networks by analyzing the changes of several different categories of equiconcepts, when the nodes change.
Y. Yang, F. Hao*, et.al: “Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach", IEEE Transactions on Network Science and Engineering, 2021.
WP4-Systems Evaluation, Prototype System and Demonstration
This WP mainly builds up a personalized Ads recommendation system with the proposed algorithms in WP3. Technically, this WP defined the triadic timed formal concept analysis as a new methodology to solve the problem of location and context-aware advertisement recommendation on Twitter. These research results have been published in Soft Computing and IEEE SMC 2021.
C.De Maio, M. Gallo, F. Hao, et.al: “Who and where: Context-aware Advertisement Recommendation on Twitter", Soft Computing, Vol.25 pp.379–387, 2021.
C. De Maio, M. Gallo, F. Hao, et.al: “Fine-Grained Contextaware Ad Targeting on Social Media Platforms", The IEEE International Conference on Systems, Man, and Cybernetics, pp.3059-3065 2020.
