Abstract
The depletion of natural resources in the last century now threatens our planet and the life of future generations. For the sake of sustainable development, this paper pioneers an interesting and practical problem of dense substructure (i.e., maximal cliques) mining in a fuzzy graph where the edges are weighted by the degree of membership. For parameter 0 ≤ λ ≤ 1 (also called fuzzy cut in fuzzy logic), a newly defined concept λ-maximal clique is introduced in a fuzzy graph. In order to detect the λ-maximal cliques from a fuzzy graph, an efficient mining algorithm based on Fuzzy Formal Concept Analysis (FFCA) is proposed. Extensive experimental evaluations are conducted for demonstrating the feasibility of the algorithm. In addition, a novel recommendation service based on an λ-maximal clique is provided for illustrating the sustainable usability of the problem addressed.
Highlights
Future Sustainability Computing is the computational sustainable development that meets the needs and aspirations of the present without compromising the ability of future generations to meet their own needs
(1) a social network can be established through sensoring data, and communications between individuals maybe missed or anonymized; (2) the relationships are vague in nature, such as one person influencing another in a social network; (3) and fuzzy trust relationships often occur in mobile social networks [9]
We present the methodology about Fuzzy Formal Concept Analysis (FFCA) that we adopt in this paper
Summary
Future Sustainability Computing is the computational sustainable development that meets the needs and aspirations of the present without compromising the ability of future generations to meet their own needs. It utilizes the data mining and machine learning [1,2] (e.g., supervised and unsupervised learning), decision and optimization problems [3] (e.g., linear and integer programming, dynamic programming), sequential decision making under uncertainty (e.g., Markov decision processes and recommender systems) [4,5], and networks (e.g., fuzzy graphs and network algorithms) [6,7]. We design a novel approach on topological structure mining of social networks for future computational sustainability
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