國立虎尾科技大學 |

Social network large-scale decision-making = developing decision support methods at scale and social networks /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Social network large-scale decision-making/ by Zhijiao Du, Sumin Yu.
其他題名:	developing decision support methods at scale and social networks /
作者:	Du, Zhijiao.
其他作者:	Yu, Sumin.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xxii, 141 p. :illustrations, digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Operations Management. -
電子資源:	https://doi.org/10.1007/978-981-99-7794-9
ISBN:	9789819977949

Social network large-scale decision-making = developing decision support methods at scale and social networks /
Du, Zhijiao.

Social network large-scale decision-makingdeveloping decision support methods at scale and social networks /[electronic resource] :by Zhijiao Du, Sumin Yu. - Singapore :Springer Nature Singapore :2023. - xxii, 141 p. :illustrations, digital ;24 cm. - Uncertainty and operations research,2195-9978. - Uncertainty and operations research..

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.

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.

ISBN: 9789819977949

Standard No.: 10.1007/978-981-99-7794-9doiSubjects--Topical Terms:

1069063
Operations Management.

LC Class. No.: HD30.23

Dewey Class. No.: 658.4036

Social network large-scale decision-making = developing decision support methods at scale and social networks /
LDR:03521nam a2200361 a 4500 001 1119560
003 DE-He213
005 20231123190244.0
006 m d
007 cr nn 008maaau
008 240612s2023 si s 0 eng d
020 $a 9789819977949 $q (electronic bk.)
020 $a 9789819977932 $q (paper)
024 7 $a 10.1007/978-981-99-7794-9 $2 doi
035 $a 978-981-99-7794-9
040 $a GP $c GP
041 0 $a eng
050 4 $a HD30.23
072 7 $a KJT $2 bicssc
072 7 $a KJMD $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 658.4036 $2 23
090 $a HD30.23 $b .D812 2023
100 1 $a Du, Zhijiao. $3 1434369
245 1 0 $a Social network large-scale decision-making $h [electronic resource] : $b developing decision support methods at scale and social networks / $c by Zhijiao Du, Sumin Yu.
260 $a Singapore : $c 2023. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xxii, 141 p. : $b illustrations, digital ; $c 24 cm.
490 1 $a Uncertainty and operations research, $x 2195-9978
505 0 $a 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.
520 $a 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.
650 2 4 $a Operations Management. $3 1069063
650 1 4 $a Operations Research and Decision Theory. $3 1366301
650 0 $a Online social networks $x Decision making. $3 1434371
650 0 $a Group decision making. $3 570427
700 1 $a Yu, Sumin. $3 1434370
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Uncertainty and operations research. $3 1062258
856 4 0 $u https://doi.org/10.1007/978-981-99-7794-9
950 $a Business and Management (SpringerNature-41169)