Social Network Large Scale Decision Making

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.

This book investigates in detail large-scale group decision-making (LSGDM) problem, which has gradually evolved from the traditional group decision-making problem and has attracted more and more attention in the age of big data. Pursuing a holistic approach, the book establishes a fundamental framework for LSGDM with uncertain and behavioral considerations. To address the behavioral uncertainty and complexity of large groups of decision-makers, this book mainly focuses on new solutions of LSGDM problems using the interval type-2 fuzzy uncertainty theory and social network analysis techniques, including the exploration of uncertain clustering analysis, the consideration of social relationships, especially trust relationships, the construction of consensus evolution networks, etc. The book is intended for researchers and postgraduates who are interested in complex group decision-making in the new media era. Authors also investigate the similar features between LSGDM problems and group recommendations to study the applications of LSGDM methods. After reading this book, readers will have a new understanding of the LSGDM study under the real complicated context.

This book explores clustering operations in the context of social networks and consensus-reaching paths that take into account non-cooperative behaviors. This book focuses on the two key issues in large-scale group decision-making: clustering and consensus building. Clustering aims to reduce the dimension of a large group. Consensus reaching requires that the divergent individual opinions of the decision makers converge to the group opinion. This book emphasizes the similarity of opinions and social relationships as important measurement attributes of clustering, which makes it different from traditional clustering methods with single attribute to divide the original large group without requiring a combination of the above two attributes. The proposed consensus models focus on the treatment of non-cooperative behaviors in the consensus-reaching process and explores the influence of trust loss on the consensus-reaching process.The logic behind is as follows: firstly, a clustering algorithm is adopted to reduce the dimension of decision-makers, and then, based on the clusters’ opinions obtained, a consensus-reaching process is carried out to obtain a decision result acceptable to the majority of decision-makers. Graduates and researchers in the fields of management science, computer science, information management, engineering technology, etc., who are interested in large-scale group decision-making and consensus building are potential audience of this book. It helps readers to have a deeper and more comprehensive understanding of clustering analysis and consensus building in large-scale group decision-making.

Social network analysis has created novel opportunities within the field of data science. The complexity of these networks requires new techniques to optimize the extraction of useful information. Graph Theoretic Approaches for Analyzing Large-Scale Social Networks is a pivotal reference source for the latest academic research on emerging algorithms and methods for the analysis of social networks. Highlighting a range of pertinent topics such as influence maximization, probabilistic exploration, and distributed memory, this book is ideally designed for academics, graduate students, professionals, and practitioners actively involved in the field of data science.

This SpringerBrief provides a pioneering, central point of reference for the interested reader in Large Group Decision Making trends such as consensus support, fusion and weighting of relevant decision information, subgroup clustering, behavior management, and implementation of decision support systems, among others. Based on the challenges and difficulties found in classical approaches to handle large decision groups, the principles, families of techniques, and newly related disciplines to Large-Group Decision Making (such as Data Science, Artificial Intelligence, Social Network Analysis, Opinion Dynamics, Behavioral and Cognitive Sciences), are discussed. Real-world applications and future directions of research on this novel topic are likewise highlighted.

The integration of applied intelligence with software has been an essential enabler for science and the new economy, creating new possibilities for a more reliable, flexible and robust society. But current software methodologies, tools, and techniques often fall short of expectations, and are not yet sufficiently robust or reliable for a constantly changing and evolving market. This book presents the proceedings of SoMeT_22, the 21st International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, held from 20 - 22 September 2022 in Kitakyushu, Japan. The SoMeT conference provides a platform for the exchange of ideas and experience in the field of software technology, with the emphasis on human-centric software methodologies, end-user development techniques, and emotional reasoning for optimal performance. The 58 papers presented here were each carefully reviewed by 3 or 4 referees for technical soundness, relevance, originality, significance and clarity, they were then revised before being selected by the international reviewing committee. The papers are arranged in 9 chapters: software systems with intelligent design; software systems security and techniques; formal techniques for system software and quality assessment; applied intelligence in software; intelligent decision support systems; cyber-physical systems; knowledge science and intelligent computing; ontology in data and software; and machine learning in systems software. The book assembles the work of scholars from the international research community to capture the essence of the new state-of-the-art in software science and its supporting technology, and will be of interest to all those working in the field.

Emerging Trends in Smart Societies: Interdisciplinary Perspectives” captures the essence of the groundbreaking initiative heralded by the inaugural International Conference on Humanities for Smart Societies 2023 (HMSS 23). This milestone event convenes a global cohort of scholars, policymakers, and thinkers, transcending geographical confines via a pioneering virtual platform. The book crystallizes the convergence of diverse disciplines – from humanities to management – fostering an exchange of innovative ideas vital for sustainable, digitally transformed societies. By orchestrating cross-disciplinary dialogues, this anthology unveils novel solutions and holistic approaches to contemporary challenges.