Reliability-driven joint clustering based on hybrid attribute analysis for supporting social network large-scale decision-making

Abstract

Social network large-scale decision-making (SNLSDM) has become an important domain in the field of decision science. A major challenge in solving such problems lies in the effective data dimensionality reduction through clustering techniques. While the reliability of evaluation information significantly influences clustering quality, most clustering algorithms overlook this critical factor. To address this gap, this article proposes novel reliability-driven joint clustering algorithms based on hybrid attribute analysis for SNLSDM problems. First, the probabilistic linguistic evaluation-reliability function (PL-ERF) is defined to handle fuzzy evaluations and social networks, along with its operational rules. We describe the configuration of SNLSDM with PL-ERFs. Subsequently, the hybrid attribute analysis is conducted to process the initial trust social network. A new reliability-based trust propagation method is designed to construct a complete trust social network. By combining trust and similarity information, a compatibility network is established. Accordingly, we develop reliability-driven joint clustering algorithms that consider multiple constraints, including similarity, trust, and compatibility. We also discuss the time complexity analysis and scalability of the algorithms. Finally, a numerical experiment and two real-word cases studies illustrate the feasibility and effectiveness of the algorithms. A comparative analysis highlights the impact and advantages of incorporating reliability into clustering.