Social Network Large-Scale Decision-Making: Developing Decision Support Methods at Scale and Social Networks

ISBN-13: 9789819977932 / Angielski

Zhijiao Du; Sumin Yu

Spis treści

Introduction.- Preliminary Knowledge.- Trust and Behavior Analysis-based Structure-heterogeneous Information Fusion.- Trust Cop-Kmeans Clustering Method.- Compatibility Distance Oriented Off-center Clustering Method.- Minimum-Cost Consensus Model Considering Trust Loss.- Punishment-Driven Consensus-Reaching Model Considering Trust Loss.- Practical Applications.- Conclusions and Future Research Directions.

O autorze

Zhijiao Du is a Faculty-Appointed Assistant Professor and Postdoctoral Fellow at the Business School, Sun Yat-Sen University, Shenzhen, China. Dr. Du received his B.S. degree in Management from Hainan University in 2012, his M.S. degree in Management from Central South University in 2016, and his Ph.D degree in Management from Sun Yat-Sen University in 2022. He chaired a project of the National Natural Science Foundation of China, a project funded by China Postdoctoral Science Foundation and a project of Guangdong Provincial Philosophy and Social Science Planning. His research interests include social network big data analysis, intelligent group decision-making, large-scale decision-making and consensus, digital supply chain finance, corporate venture capital, etc. He has now published more than 20 academic articles in top journals and conference proceedings, including Decision Support Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Computational Social Systems, Information Fusion, Information Sciences, Knowledge-Based Systems, Computers & Industrial Engineering, Group Decision and Negotiation, among others. Two of the articles was selected into ESI Global Database of Highly Cited Papers in Computer Science. He co-authored a book published in Springer. His h-index is 11 with more than 860 citations received in Google Scholar. He serves as a reviewer in many top-tier international journals in related areas to decision analysis, supply chain management.

Sumin Yu is an Associate Professor, Distinguished Associate Research Fellow and Master Supervisor at the College of Management, Shenzhen University, Shenzhen, China. Yu received her Ph.D degree in Management from Central South University in 2018. Her research areas include electronic commerce, information management, decision theory and methods, large-scale decision-making and consensus, big data decision, social network analysis, tourism management, etc. She chaired a project of National Natural Science Foundation. She has now published more than 30 international journal papers in top journals and conference proceedings, including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Computational Social Systems, Information Fusion, Information Sciences, Knowledge-Based Systems, Applied Soft Computing, Computers & Industrial Engineering, Group Decision and Negotiation, International Transactions in Operational Research, among others. She co-authored a book published in Springer. Her h-index is 15 with more than 1100 citations received in Google Scholar. A total of 4 articles were selected into ESI Global High Citation Paper Database, among which 2 articles were selected as hot papers. She serves as a reviewer in many top-tier international journals in related areas to fuzzy decision-making, soft computing, large-scale decision-making.

 

Blurb

This book focuses on the following three key topics in social network large-scale decision-making: structure-heterogeneous information fusion, clustering analysis with multiple measurement attributes, and consensus building considering trust loss. To address the aggregation and distance measurement of structure-heterogeneous evaluation information, we propose a fusion method based on trust and behavior analysis. Then, two clustering algorithms are put forward, including trust Cop-K-means clustering algorithm and compatibility distance-oriented off-center clustering algorithm. The above clustering algorithms emphasize the similarity of opinions and social relationships as important measurement attributes of clustering. Finally, this book explores the impact of trust loss originating from social relationships on the CRP and develops two consensus-reaching models, namely the improved minimum-cost consensus model that takes into account voluntary trust loss and the punishment-driven consensus-reaching model. Some case studies, a large number of numerical experiments, and comparative analyses are provided in this book to demonstrate the characteristics and advantages of the proposed methods and models. 

The authors encourage researchers, students, and enterprises engaged in social network analysis, group decision-making, multi-agent collaborative decision-making, and large-scale data processing to pay attention to the proposals presented in this book. After reading this book, the authors expect readers to have a deeper and more comprehensive understanding of social network large-scale decision-making. Inorder to make it more accurate for readers to understand the methods and models presented in this book, the authors strongly recommend that potential readers have a good research foundation in fuzzy soft computing, traditional clustering algorithms, basic mathematics knowledge, and other related preliminaries.