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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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
In the emerging crowd cyber-eco systems, 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, which provides important guidelines on the design of reliable networks capable of predicting and preventing detrimental events with negative impacts on our society and economy. This chapter introduces basic concepts in behavior and evolutionary dynamics in crowd networks. Using information diffusion over social networks as an example, we discuss challenges in the modeling and analysis of user behavior and their interactions in large-scale, decentralized and heterogeneous networks, and introduce an evolutionary game theoretic framework to study behavior dynamics in crowd networks.
Yan Chen, H. Vicky Zhao

Chapter 2. Evolutionary Dynamics with Rational Users

Abstract
Social networks have become ubiquitous in our daily life, and people are now used to interacting and sharing information through social networks. Understanding the mechanisms of tremendous information propagation over social networks is critical to various applications such as online advertisement and rumour control. In this chapter, we focus on the evolutionary game-theoretic model for information diffusion among rational users in social network, and analyze evolutionary dynamics under several different scenarios. By applying graphical evolutionary game theory (EGT) to information diffusion, we could predict every small change in the process, get the detailed dynamics and finally foretell the stable states.
Yan Chen, H. Vicky Zhao

Chapter 3. “Irrational” Behavior Analysis

Abstract
Social networks play an important role in our daily life, and we utilize them to contact with others as well as spreading different kinds of information every day. While enjoying the convenience of social networks, we have to acknowledge that they create some security problems. Wrong, misleading or even harmful information, virus for example, is released and disseminated by malicious users over social networks, which lead to bad influences and severe consequences. Therefore, it is necessary to understand the process of information diffusion and figure out the hazard of malicious users to the whole social network. In this chapter, we employ graphical evolutionary game theory (EGT) to investigate the negative impacts caused by malicious users in information diffusion over social networks, by theoretically analyzing the population dynamics and evolutionary stable strategies. Experiments on synthetic networks, Facebook networks and real-world microblog data set are conducted, and results validate the theoretic derivation.
Yan Chen, H. Vicky Zhao

Chapter 4. “Smart” Evolution with Indirect Reciprocity

Abstract
When enjoying the convenience of social networks, we are encountering the harm caused by malicious users in social networks as well. In order to reduce their negative effects, it is essential for rational users to carefully screen each connected neighbor to protect themselves from malicious users, implying that establishing a rule for users’ interactions in order to mitigate malicious users’ influences is required. This chapter introduces the reputation mechanism and proposes a smart evolution model based on evolutionary game theory with indirect reciprocity. The model takes into account both the current reputation and instant incentives in users’ decision-making process. After social norms and reputation updating policy are defined, we theoretically analyze the evolutionary dynamics and corresponding evolutionary stable state (ESS) under the proposed scheme. Finally, the validity of the smart evolution model is verified by simulations on synthetic networks, Facebook networks and real-world microblog data set.
Yan Chen, H. Vicky Zhao

Chapter 5. Diffusion of Multi-source Correlated Information

Abstract
Recently, online social networks are playing an ever-important role in both our social life and economy. Therefore, modeling of information diffusion over social networks is a common research topic, which is of crucial importance to better understand how the avalanche of information overflow leads to the detrimental consequences, and how to motivate some beneficial information spreading. However, most model-based works on information diffusion either merely consider the spreading of one single message or make the assumption that different diffusion processes are independent of each other. In real-world scenarios, multi-source correlated information often spread together, which jointly influence users’ decisions. In this chapter, we aim to model the multi-source information diffusion processes from a graphical evolutionary game perspective. Specifically, we model users’ local interactions and strategic decision making process, and analyze the evolutionary dynamics of the diffusion processes of correlated information. We conduct simulations on both synthetic and Facebook real-world networks, and the simulation results are consistent with our theoretical analysis. We also test the model on the users’ forwarding data in “Weibo” social networks and observe an effective prediction performance on the real-world information spreading processes.
Yan Chen, H. Vicky Zhao

Chapter 6. Analysis of Super Users in Information Diffusion

Abstract
Modeling and analysis of information propagation over social networks are of great significance to better understand the avalanche of information flow and to investigate its impact on the economy and our daily life. In social networks, there exist some “super users” who have higher social status and potentially larger influence. In this chapter, we extend the graphical evolutionary game-theoretic framework to investigate their impact on information diffusion by analyzing the evolutionary dynamics and stable states. Simulation results over synthetic networks are consistent with our theoretical analysis, and demonstrate that when super users update their strategies as others do, they have little influence on the spread of information. However, when they insist on their strategies and keep forwarding the information, they have a huge impact on information propagation.
Yan Chen, H. Vicky Zhao

Chapter 7. Concluding Remarks

Abstract
The crowd networks include millions of deeply connected individuals, smart devices, government agencies, organizations, and enterprises, who actively interact with each other and influence each other’s decisions. This book provides a holistic framework to study their decision-making processes and their interactions and analyze its impact on the crowd networks. This investigation offers critical guidelines on crowd networks’ design to avoid detrimental events that affect our society and economy. This chapter reviews essential findings in each chapter and points out a few possible future directions. We aim to encourage researchers from different disciplines to address the challenging issues and explore the untouched territories in this emerging research field.
Yan Chen, H. Vicky Zhao

Backmatter

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