Social Network Large Scale Decision Making

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Social Network Large-Scale Decision-Making

Author : Zhijiao Du
Publisher : Springer Nature
ISBN 13 : 9819977940
Total Pages : 157 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Social Network Large-Scale Decision-Making by : Zhijiao Du

Download or read book Social Network Large-Scale Decision-Making written by Zhijiao Du and published by Springer Nature. This book was released on 2023-12-25 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Large-Scale Group Decision-Making with Uncertain and Behavioral Considerations

Author : Tong Wu
Publisher : Springer Nature
ISBN 13 : 9811981671
Total Pages : 372 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Large-Scale Group Decision-Making with Uncertain and Behavioral Considerations by : Tong Wu

Download or read book Large-Scale Group Decision-Making with Uncertain and Behavioral Considerations written by Tong Wu and published by Springer Nature. This book was released on 2023-01-23 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Large Group Decision Making

Author : Iván Palomares Carrascosa
Publisher : Springer
ISBN 13 : 3030010279
Total Pages : 118 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Large Group Decision Making by : Iván Palomares Carrascosa

Download or read book Large Group Decision Making written by Iván Palomares Carrascosa and published by Springer. This book was released on 2018-10-31 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Large-Scale Group Decision-Making

Author : Su-Min Yu
Publisher : Springer Nature
ISBN 13 : 9811678898
Total Pages : 195 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Large-Scale Group Decision-Making by : Su-Min Yu

Download or read book Large-Scale Group Decision-Making written by Su-Min Yu and published by Springer Nature. This book was released on 2022-01-03 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Graph Theoretic Approaches for Analyzing Large-Scale Social Networks

Author : Meghanathan, Natarajan
Publisher : IGI Global
ISBN 13 : 1522528156
Total Pages : 376 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Graph Theoretic Approaches for Analyzing Large-Scale Social Networks by : Meghanathan, Natarajan

Download or read book Graph Theoretic Approaches for Analyzing Large-Scale Social Networks written by Meghanathan, Natarajan and published by IGI Global. This book was released on 2017-07-13 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Behavior and Evolutionary Dynamics in Crowd Networks

Author : Yan Chen
Publisher : Springer Nature
ISBN 13 : 9811571600
Total Pages : 148 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Behavior and Evolutionary Dynamics in Crowd Networks by : Yan Chen

Download or read book Behavior and Evolutionary Dynamics in Crowd Networks written by Yan Chen and published by Springer Nature. This book was released on 2020-07-28 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a holistic framework to study behavior and evolutionary dynamics in large-scale, decentralized, and heterogeneous crowd networks. In the emerging crowd cyber-ecosystems, millions of deeply connected individuals, smart devices, government agencies, and enterprises actively interact with each other and influence each other’s decisions. It is crucial to understand such intelligent entities’ behaviors and to study their strategic interactions in order to provide important guidelines on the design of reliable networks capable of predicting and preventing detrimental events with negative impacts on our society and economy. This book reviews the fundamental methodologies to study user interactions and evolutionary dynamics in crowd networks and discusses recent advances in this emerging interdisciplinary research field. Using information diffusion over social networks as an example, it presents a thorough investigation of the impact of user behavior on the network evolution process and demonstrates how this can help improve network performance. Intended for graduate students and researchers from various disciplines, including but not limited to, data science, networking, signal processing, complex systems, and economics, the book encourages researchers in related research fields to explore the many untouched areas in this domain, and ultimately to design crowd networks with efficient, effective, and reliable services.

Modeling Complex Linguistic Information to Support Group Decision Making Under Uncertainty

Author : Zhen Zhang
Publisher : Springer Nature
ISBN 13 : 981973584X
Total Pages : 204 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis Modeling Complex Linguistic Information to Support Group Decision Making Under Uncertainty by : Zhen Zhang

Download or read book Modeling Complex Linguistic Information to Support Group Decision Making Under Uncertainty written by Zhen Zhang and published by Springer Nature. This book was released on with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Computational Perspectives on Large-scale Social Networks

Author : Johan Holke Olof Ugander
Publisher :
ISBN 13 :
Total Pages : 195 pages
Book Rating : 4.:/5 (9 download)

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Book Synopsis Computational Perspectives on Large-scale Social Networks by : Johan Holke Olof Ugander

Download or read book Computational Perspectives on Large-scale Social Networks written by Johan Holke Olof Ugander and published by . This book was released on 2014 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates both how computational perspectives can improve our understanding of social networks, and also how modern insights about social networks can be put to work to address difficult computational and inferential challenges across systems engineering and the social sciences. The microstructure of human behavior has a rich history of study across many disciplines, and only recently - through the data deluge of online instrumentation and experimentation - has the role networks play across social and economic domains come into full view. Work in this thesis examines how social network neighborhoods, the rich local networks that surround individuals, function as contact surfaces through which individuals process information, mediating social decision and social contagion processes. Work in this thesis on distributing graph computations at Facebook, the online social networking service, has led to dramatic efficiency gains there, successfully deploying a new partitioning algorithm to reduce average query times for their "People You May Know" link prediction system by 50%. These improvements were achieved by harnessing both geographic and network structures of social graphs not necessarily found in other graph contexts. Additional work presents a highly scalable "restreaming" approach to partitioning massive graphs with rich local structure. Lastly, work on interference in online experiments (A/B tests) offers a framework based on graph partitioning to design lower variance estimators for treatment effects under network interference, a grand challenge of modern online experimentation. As individuals bring their social relations online, the web is rapidly evolving from a network of documents to a network of people, and computing with social data will require richer, novel methods for working with the subtleties that give social networks their distinctive character.

Group Decision Making and Preference Learning on Social Networks

Author : Amirali Salehi-Abari
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Group Decision Making and Preference Learning on Social Networks by : Amirali Salehi-Abari

Download or read book Group Decision Making and Preference Learning on Social Networks written by Amirali Salehi-Abari and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on exploiting the dynamics and correlations of preferences over social networks for developing efficient group decision making systems. One of the main challenges in any group decision problem is learning the individual preferences upon which decisions are based. It is the position of this thesis that social networks--by capturing preference correlations across individuals induced by social interactions--provide a natural and informative platform for preference learning. By mathematical modelling of preference dynamics and correlations over social networks, we focus on developing efficient algorithms for group decision making and recommendations, with less required user data, and lower cognitive and communication burden. We introduce empathetic frameworks in which individuals derive utility based on both their own intrinsic preferences (or happiness) and empathetic preferences, determined by the satisfaction of their acquaintances. After theoretically analysing this framework, we develop a scalable algorithm for group recommendation, and empirically demonstrate its performance. To capture the correlation of preference rankings on social networks, we introduce a network formation model called ranking networks in which the similarity of two individuals' rankings determines the chance they are connected to each other. After a thorough theoretical analysis, we use a special instance that we call preference-oriented social networks, for group decision making when faced with missing preferences. We develop algorithms to predict unknown individual preferences given known preferences of others in the social network and to make effective group decisions with partial preferences. Our empirical results demonstrate that incorporating social ties can significantly improve predictions and group decision making.

New Trends in Intelligent Software Methodologies, Tools and Techniques

Author : H. Fujita
Publisher : IOS Press
ISBN 13 : 1643683179
Total Pages : 744 pages
Book Rating : 4.6/5 (436 download)

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Book Synopsis New Trends in Intelligent Software Methodologies, Tools and Techniques by : H. Fujita

Download or read book New Trends in Intelligent Software Methodologies, Tools and Techniques written by H. Fujita and published by IOS Press. This book was released on 2022-10-11 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Social Network DeGroot Model

Author : Yucheng Dong
Publisher : Springer Nature
ISBN 13 : 9819704219
Total Pages : 175 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis Social Network DeGroot Model by : Yucheng Dong

Download or read book Social Network DeGroot Model written by Yucheng Dong and published by Springer Nature. This book was released on with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Advances Towards a Societal-scale Decision-support System

Author : Marko Rodriguez
Publisher :
ISBN 13 :
Total Pages : 114 pages
Book Rating : 4.:/5 (71 download)

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Book Synopsis Advances Towards a Societal-scale Decision-support System by : Marko Rodriguez

Download or read book Advances Towards a Societal-scale Decision-support System written by Marko Rodriguez and published by . This book was released on 2004 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Emerging Trends in Smart Societies

Author : Worakamol Wisetsri
Publisher : Taylor & Francis
ISBN 13 : 1040045944
Total Pages : 454 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Emerging Trends in Smart Societies by : Worakamol Wisetsri

Download or read book Emerging Trends in Smart Societies written by Worakamol Wisetsri and published by Taylor & Francis. This book was released on 2024-06-24 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Granularities-Driven Hesitant Fuzzy Linguistic Decision Making

Author : Yuanhang Zheng
Publisher : Springer Nature
ISBN 13 : 3031603508
Total Pages : 201 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis Granularities-Driven Hesitant Fuzzy Linguistic Decision Making by : Yuanhang Zheng

Download or read book Granularities-Driven Hesitant Fuzzy Linguistic Decision Making written by Yuanhang Zheng and published by Springer Nature. This book was released on with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt:

New Approaches to Maximizing Influence in Large-scale Social Networks

Author : David Scott Hunter (Ph.D.)
Publisher :
ISBN 13 :
Total Pages : 182 pages
Book Rating : 4.:/5 (12 download)

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Book Synopsis New Approaches to Maximizing Influence in Large-scale Social Networks by : David Scott Hunter (Ph.D.)

Download or read book New Approaches to Maximizing Influence in Large-scale Social Networks written by David Scott Hunter (Ph.D.) and published by . This book was released on 2020 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the widespread adoption of social media in today’s society, the problem of identifying the most influential individuals whose adoption of a product or action will spread maximally in the network is of increased practical significance. This thesis considers new strategies and methods for this problem, which is known as the influence maximization problem, focusing on a setting where the influence is determined by some function of user opinions. In the first chapter, we introduce a new model of opinion dynamics that is motivated by research in both social psychology and political science. We present a series of theoretical results concerning the convergence of the opinions to an equilibrium, including conditions under which convergence to a fixed point occurs, an explicit characterization of the equilibrium, and the rate of convergence to the equilibrium. In the second chapter, we propose new approaches to the influence maximization problem in a social network when the dynamics adhere to the model in the first chapter. We consider applying these methods on several large-scale real-world social networks. In doing so, we attempt to measure the validity of the model we propose, consider estimating the relative importance of some special users via a centrality function approach, and highlight the computational efficiency of our influence maximization methods. In the final chapter, we introduce an alternative approach to maximizing influence in a social network that has as a solution a dynamic policy that considers when, what, and with whom an agent communicates. We motivate the necessity for a dynamic policy solution by highlighting some realistic behaviors that make modeling and analyzing real-world dynamics difficult. By leveraging reinforcement learning, we learn policies that account for some of these realistic behaviors and find that these policies exhibit impressive performance on large-scale networks.

EdgeAI for Algorithmic Government

Author : Rajan Gupta
Publisher : Springer Nature
ISBN 13 : 9811997985
Total Pages : 109 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis EdgeAI for Algorithmic Government by : Rajan Gupta

Download or read book EdgeAI for Algorithmic Government written by Rajan Gupta and published by Springer Nature. This book was released on 2023-03-26 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides various EdgeAI concepts related to its architecture, key performance indicators, and enabling technologies after introducing algorithmic government, large-scale decision-making, and computing issues in the cloud and fog. With advancements in technology, artificial intelligence has permeated our personal lives and the fields of economy, socio-culture, and politics. The integration of artificial intelligence (AI) into decision-making for public services is changing how governments operate worldwide. This book discusses how algorithms help the government in various ways, including virtual assistants for busy civil servants, automated public services, and algorithmic decision-making processes. In such cases, the implementation of algorithms will occur on a massive scale and possibly affect the lives of entire communities. The cloud-centric architecture of artificial intelligence brings out challenges of latency, overhead communication, and significant privacy risks. Due to the sheer volume of data generated by IoT devices, the data analysis must be performed at the forefront of the network. This introduces the need for edge computing in algorithmic government. EdgeAI, the confluence of edge computing and AI, is the primary focus of this book. It also discusses how one can incorporate these concepts in algorithmic government through conceptual framework and decision points. Finally, the research work emphasizes some design challenges in edge computing from applications viewpoint. This book will be helpful for data engineers, data scientists, cloud engineers, data management experts, public policymakers, administrators, research scholars and academicians.

Complex Spreading Phenomena in Social Systems

Author : Sune Lehmann
Publisher : Springer
ISBN 13 : 3319773321
Total Pages : 356 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Complex Spreading Phenomena in Social Systems by : Sune Lehmann

Download or read book Complex Spreading Phenomena in Social Systems written by Sune Lehmann and published by Springer. This book was released on 2018-06-21 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is about spreading of information and influence in complex networks. Although previously considered similar and modeled in parallel approaches, there is now experimental evidence that epidemic and social spreading work in subtly different ways. While previously explored through modeling, there is currently an explosion of work on revealing the mechanisms underlying complex contagion based on big data and data-driven approaches. This volume consists of four parts. Part 1 is an Introduction, providing an accessible summary of the state of the art. Part 2 provides an overview of the central theoretical developments in the field. Part 3 describes the empirical work on observing spreading processes in real-world networks. Finally, Part 4 goes into detail with recent and exciting new developments: dedicated studies designed to measure specific aspects of the spreading processes, often using randomized control trials to isolate the network effect from confounders, such as homophily. Each contribution is authored by leading experts in the field. This volume, though based on technical selections of the most important results on complex spreading, remains quite accessible to the newly interested. The main benefit to the reader is that the topics are carefully structured to take the novice to the level of expert on the topic of social spreading processes. This book will be of great importance to a wide field: from researchers in physics, computer science, and sociology to professionals in public policy and public health.