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About this book

This self-contained book describes social influence from a computational point of view, with a focus on recent and practical applications, models, algorithms and open topics for future research. Researchers, scholars, postgraduates and developers interested in research on social networking and the social influence related issues will find this book useful and motivating. The latest research on social computing is presented along with and illustrations on how to understand and manipulate social influence for knowledge discovery by applying various data mining techniques in real world scenarios. Experimental reports, survey papers, models and algorithms with specific optimization problems are depicted. The main topics covered in this book are: chrematistics of social networks, modeling of social influence propagation, popular research problems in social influence analysis such as influence maximization, rumor blocking, rumor source detection, and multiple social influence competing.

Table of Contents

Frontmatter

Chapter 1. Introduction of Social Influence Analysis

Abstract
With the emergence and rapid proliferation of social applications and media, such as instant messaging (e.g., WhatsApp, Viber, WeChat, Snapchat, Line, Facebook Messenger, and Google Hangouts), sharing sites (e.g., Flickr, YouTube, and Yelp), blogs (e.g., WordPress and LiveJournal), wikis (e.g., Wikipedia and PBWiki), microblogs (e.g., Twitter and Weibo), social networks (e.g., Facebook), and collaboration networks (e.g., DBLP), there is little doubt that social influence is becoming a prevalent, complex, and subtle force that governs the dynamics of all social networks. Therefore, social influence study has started to attract intense attention due to many important applications.
Wen Xu, Weili Wu

Chapter 2. Diffusion of Information

Abstract
In this chapter, we outline the techniques used in optimizing or facilitating information diffusion in social networks. We identify two problem definitions through which a broad survey of techniques in recent research is provided. Namely, we explore the problems of maximizing the spread of influence and minimizing the spread of misinformation in social networks. As different as these problems are in terms of the motivation behind them, they both rely on sub-problems that are very similar. Through our study of these two problems, we delve into more detail about the sub-problems: Sect. 2.2 model formation, Sect. 2.3 problem optimization, Sect. 2.4 large-scale data analysis, and Sect. 2.5 research trends.
Wen Xu, Weili Wu

Chapter 3. Information Source Detection in Social Networks

Abstract
The rising popularity of online social networks has made information generating and sharing much easier than ever before, due to the ability to publish content to large, targeted audiences. Such networks enable their participants to simultaneously become both consumers and producers of content, shifting the role of information broker from a few dedicated entities to a diverse and distributed group of individuals. While this fundamental change allows information propagating at an unprecedented rate, it also enables unreliable or unverified information spreading among people, such as rumors.
Wen Xu, Weili Wu

Chapter 4. Rumor Blocking in Social Networks

Abstract
Online social networks have many benefits as a medium for fast, widespread information dissemination. They provide fast access to large-scale news data, sometimes even before the mass media. They also serve as a medium to collectively achieve a social goal. For instance with the use of group and event pages in Facebook, events such as Day of Action protests reached thousands of protestors. While the ease of information propagation in social networks can be very beneficial, it can also have disruptive effects. One such example was observed in August 2012, thousands of people in Ghazni province left their houses in the middle of the night in panic after the rumor of earthquake. Another example is the fast spread of misinformation in twitter that the president of Syria is dead, leading to a sharp, quick increase in the price of oil. There are lots of similar examples. Although social networks are the main source of news for many people today, they are not considered reliable due to such problems.
Wen Xu, Weili Wu

Chapter 5. Multiple Social Influence: Models and Applications

Abstract
Cascading processes are models of network diffusion used to study phenomenon concerning the spread of new trends and innovations in social networks. Each node can be in one of two states: infected (i.e., supports an idea or a product) or uninfected. Every infected node can infect its neighbors and thus, the infection, formally called a cascade, propagates through the network. These processes have been studied in many applications such as viral marketing, blog networks, and contagion models.
Wen Xu, Weili Wu

Backmatter

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