Information Diffusion And Influence Propagation On Social Networks With Marketing Applications

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Information and Influence Propagation in Social Networks

Author : Wei Chen
Publisher : Springer Nature
ISBN 13 : 3031018508
Total Pages : 161 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Information and Influence Propagation in Social Networks by : Wei Chen

Download or read book Information and Influence Propagation in Social Networks written by Wei Chen and published by Springer Nature. This book was released on 2022-05-31 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on social networks has exploded over the last decade. To a large extent, this has been fueled by the spectacular growth of social media and online social networking sites, which continue growing at a very fast pace, as well as by the increasing availability of very large social network datasets for purposes of research. A rich body of this research has been devoted to the analysis of the propagation of information, influence, innovations, infections, practices and customs through networks. Can we build models to explain the way these propagations occur? How can we validate our models against any available real datasets consisting of a social network and propagation traces that occurred in the past? These are just some questions studied by researchers in this area. Information propagation models find applications in viral marketing, outbreak detection, finding key blog posts to read in order to catch important stories, finding leaders or trendsetters, information feed ranking, etc. A number of algorithmic problems arising in these applications have been abstracted and studied extensively by researchers under the garb of influence maximization. This book starts with a detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena. We describe their properties as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. We delve deep into the key problem of influence maximization, which selects key individuals to activate in order to influence a large fraction of a network. Influence maximization in classic diffusion models including both the independent cascade and the linear threshold models is computationally intractable, more precisely #P-hard, and we describe several approximation algorithms and scalable heuristics that have been proposed in the literature. Finally, we also deal with key issues that need to be tackled in order to turn this research into practice, such as learning the strength with which individuals in a network influence each other, as well as the practical aspects of this research including the availability of datasets and software tools for facilitating research. We conclude with a discussion of various research problems that remain open, both from a technical perspective and from the viewpoint of transferring the results of research into industry strength applications.

Information Diffusion and Influence Propagation on Social Networks with Marketing Applications

Author : Jiesi Cheng
Publisher :
ISBN 13 :
Total Pages : 124 pages
Book Rating : 4.:/5 (867 download)

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Book Synopsis Information Diffusion and Influence Propagation on Social Networks with Marketing Applications by : Jiesi Cheng

Download or read book Information Diffusion and Influence Propagation on Social Networks with Marketing Applications written by Jiesi Cheng and published by . This book was released on 2013 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Web and mobile technologies have had such profound impact that we have witnessed significant evolutionary changes in our social, economic and cultural activities. In recent years, online social networking sites such as Twitter, Facebook, Google+, and LinkedIn have gained immense popularity. Such social networks have led to an enormous explosion of network-centric data in a wide variety scenarios, posing unprecedented analytical and computational challenges to MIS researchers. At the same time, the availability of such data offers major research opportunities in various social computing and analytics areas to tackle interesting questions such as: From a business and marketing perspective, how to mine the novel datasets of online user activities, interpersonal communications and interactions, for developing more successful marketing strategies? From a system development perspective, how to incorporate massive amounts of available data to assist online users to find relevant, efficient, and timely information? In this dissertation, I explored these research opportunities by studying multiple analytics problems arose from the design and use of social networking services. The first two chapters (Chapter 2 and 3) are intended to study how social network can help to derive a better estimation of customer lifetime value (CLV), in the social gaming context. In Chapter 2, I first conducted an empirical study to demonstrate that friends' activities can serve as significant indicators of a player's CLV. Based on this observation, I proposed a perceptron-based online CLV prediction model considering both individual and friendship information. Preliminary results have shown that the model can be effectively used in online CLV prediction, by evaluating against other commonly-used benchmark methods. In Chapter 3, I further extended the metric of traditional CLV, by incorporating the personal influences on other customers' purchase as an integral part of the lifetime value. The proposed metric was illustrated and tested on seven social games of different genres. The results showed that the new metric can help marketing managers to achieve more successful marketing decisions in user acquisition, user retention, and cross promotion. Chapter 4 is devoted to the design of a recommendation system for micro-blogging. I studied the information diffusion pattern in a micro-blogging site (Twitter.com) and proposed diffusion-based metrics to assess the quality of micro-blogs, and leverage the new metric to implement a novel recommendation framework to help micro-blogging users to efficiently identify quality news feeds. Chapter 5 concludes this dissertation by highlighting major research contributions and future directions.

Diffusion in Social Networks

Author : Paulo Shakarian
Publisher : Springer
ISBN 13 : 3319231057
Total Pages : 110 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Diffusion in Social Networks by : Paulo Shakarian

Download or read book Diffusion in Social Networks written by Paulo Shakarian and published by Springer. This book was released on 2015-09-16 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the leading models of social network diffusion that are used to demonstrate the spread of disease, ideas, and behavior. It introduces diffusion models from the fields of computer science (independent cascade and linear threshold), sociology (tipping models), physics (voter models), biology (evolutionary models), and epidemiology (SIR/SIS and related models). A variety of properties and problems related to these models are discussed including identifying seeds sets to initiate diffusion, game theoretic problems, predicting diffusion events, and more. The book explores numerous connections between social network diffusion research and artificial intelligence through topics such as agent-based modeling, logic programming, game theory, learning, and data mining. The book also surveys key empirical results in social network diffusion, and reviews the classic and cutting-edge research with a focus on open problems.

Temporal Modeling of Information Diffusion in Online Social Networks

Author : Guolin Niu
Publisher : Open Dissertation Press
ISBN 13 : 9781361349779
Total Pages : pages
Book Rating : 4.3/5 (497 download)

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Book Synopsis Temporal Modeling of Information Diffusion in Online Social Networks by : Guolin Niu

Download or read book Temporal Modeling of Information Diffusion in Online Social Networks written by Guolin Niu and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Temporal Modeling of Information Diffusion in Online Social Networks" by Guolin, Niu, 牛国林, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The rapid development of online social networks (OSNs) renders them a powerful platform for information diffusion on a massive scale. OSNs generate enormous propagation traces. An important question is how to model the real-world information diffusion process. Although considerable studies have been conducted in this field, the temporal characteristics have not been fully addressed yet. This thesis addresses the issue of modeling the temporal dynamics of the information diffusion process. Based on empirical findings drawn from large-scale propagation traces of a popular OSN in China, we demonstrate that the temporal characteristics has a significant impact on the diffusion dynamics. Hence, a series of new temporal information diffusion models have been proposed by incorporating these temporal features. Experimental results demonstrate that these proposed models are more accurate and practical than existing discrete diffusion models. Moreover, one application of information diffusion models, i.e., the revenue maximization problem, is studied. Specifically, the thesis consists of three major parts: 1) preliminaries, i.e., introduction of research platform and collected dataset, 2) modeling social influence diffusion from three different temporal aspects, and 3) monetizing OSNs through designing intelligent pricing strategies in the diffusion process to realize the goal of revenue maximization. Firstly, the research platform is introduced and the statistical properties of the data derived from this platform are investigated. We choose Renren, the dominant social network website in China, as our research platform and study its information propagation mechanisms. Specifically, we concentrate on the propagation of "sharing video" behaviors, and collect data on more than 2.8 million Renren users and over 209 million diffusion traces. The analysis result shows that the video access patterns in OSNs differ significantly from Youtube-like systems, which makes understanding the video propagation behaviors in OSNs an important research task. Secondly, the temporal modeling of information diffusion is explored. By investigating temporal features using real diffusion traces, we find that three factors should be considered in building realistic diffusion models, including, information propagation latency, multiple influential sources and user diversities. We then develop models to explain the information propagation process by incorporating these factors, and demonstrate that the models reflect reality well. Finally, revenue maximization in the information diffusion process is studied. Specifically, the pricing factor is explicitly incorporated into the product diffusion process. To realize the goal of revenue maximization, we develop a Dynamic Programming Based Heuristic (DPBH) to obtain the optimal pricing sequence. Application of the DPBH in the revenue maximization problem shows that it performs well in both the expected revenue achieved and in running time. This leads to fundamental ramifications to many related OSN marketing applications. DOI: 10.5353/th_b5317018 Subjects: Online social networks

Python for Graph and Network Analysis

Author : Mohammed Zuhair Al-Taie
Publisher : Springer
ISBN 13 : 3319530046
Total Pages : 214 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Python for Graph and Network Analysis by : Mohammed Zuhair Al-Taie

Download or read book Python for Graph and Network Analysis written by Mohammed Zuhair Al-Taie and published by Springer. This book was released on 2017-03-20 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities. Practically, the book is suitable for courses on social network analysis in all disciplines that use social methodology. In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field. Computer scientists have the knowledge to parse and process data while sociologists have the experience that is required for efficient data editing and interpretation. Social network analysis has successfully been applied in different fields such as health, cyber security, business, animal social networks, information retrieval, and communications.

Novel Models for Influence Propagation in Social Networks

Author : Yuanjun Bi
Publisher :
ISBN 13 :
Total Pages : 196 pages
Book Rating : 4.:/5 (89 download)

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Book Synopsis Novel Models for Influence Propagation in Social Networks by : Yuanjun Bi

Download or read book Novel Models for Influence Propagation in Social Networks written by Yuanjun Bi and published by . This book was released on 2014 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: 0Influence propagation problem studies the spread of influence through a social network. One of its most important applications is viral marketing, which selects a small number of users to adopt a product. These selected users are expected to spread the influence of the product. However, traditional influence diffusion models ignore some details in the progress of influence spreading, which makes those models too simple to illustrate the real-world applications. In this dissertation, we build several novel models to study influence propagation in social networks. First, we consider the users' behavior role and influence deadline in the influence propagation progress. We extend the traditional Independent Cascade model to the Multi- chance Independent Cascade Model with users' behavior roles (MIC-R), in which users have multiple chances to influence others and their behavior roles affect their influence power. We prove that Influence Maximization problem based on MIC-R is NP (nondeterministic polynomial)-hard and the expected number of users who adopt the products is monotone and submodular. We also design an algorithm that can more effectively spread the influence than Greedy algorithms. Second, we study multiple influences spreading among social networks.We use physical charged system theory to build a charged system influence (CSI) model that describes features in social networks and the progress of influence propagation. This model loosens the constraints that influence must spread through individuals who have already been infected. Based on this model, we propose an algorithm that uses Maximal Likelihood Estimation to predict users' action in social media. Third, we study the influence propagation based on the community structure and find strategies for attracting new members to join a community. By using the community structure characters, we propose three models, Adopter Model, Benefit Model and Combine Model, to present different promotion strategies over time. A greedy algorithm is developed for expanding a community size. Finally, to expand the community more effectively, we design an algorithm that uses Coulomb theory and linear programming to choose proper seed nodes for a target community. Experiment results based on real-world datasets show that these algorithms outperform state-of-the-art methods.

STUDY OF SOCIAL-NETWORK-BASED

Author : Xiaoguang Fan
Publisher : Open Dissertation Press
ISBN 13 : 9781360999524
Total Pages : 164 pages
Book Rating : 4.9/5 (995 download)

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Book Synopsis STUDY OF SOCIAL-NETWORK-BASED by : Xiaoguang Fan

Download or read book STUDY OF SOCIAL-NETWORK-BASED written by Xiaoguang Fan and published by Open Dissertation Press. This book was released on 2017-01-26 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Study of Social-network-based Information Propagation" by Xiaoguang, Fan, 樊晓光, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Information propagation has attracted increasing attention from sociologists, marketing researchers and Information Technology entrepreneurs. With the rapid developments in online and mobile social applications like Facebook, Twitter, and LinkedIn, large-scale, high-speed and instantaneous information dissemination becomes possible, spawning tremendous opportunities for electronic commerce. It is non-trivial to make an accurate analysis on how information is propagated due to the uncertainty of human behavior and the complexity of the social environment. This dissertation is concerned with exploring models, formulations, and heuristics for the social-network-based information propagation process. It consists of three major parts: information diffusion through online social network, modeling social influence propagation, and social-network-based information spreading in opportunistic mobile networks. Firstly, I consider the problem of maximizing the influence propagation through online social networks. To solve it, I introduce a probabilistic maximum coverage problem, and propose a cluster-based heuristic and a neighbor-removal heuristic for two basic diffusion models, namely, the Linear Threshold Model and the Independent Cascade Model, respectively. Realizing that the selection of influential nodes is mainly based on the accuracy and efficiency in estimating the social influence, I build a framework of up-to-2-hop hierarchical network to approximate the spreading of social influence, and further propose a hierarchy-based algorithm to solve the influence maximization problem. Our heuristic is proved to be efficient and robust with competitive performance, low computation cost, and high scalability. The second part explores the modeling on social influence propagation. I develop an analytical model for the influence propagation process based on discrete-time Markov chains, and deduce a close-form equation to express the n-step transition probability matrix. We show that given any initial state the probability distribution of the converged network state could be easily obtained by calculating a matrix product. Finally, I study the social-network-based information spreading in opportunistic mobile networks by analyzing the opportunistic routing process. I propose three social-network-based communication pattern models and utilize them to evaluate the performance of different social-network-based routing protocols based on several human mobility traces. Moreover, I discuss the fairness evaluation in opportunistic routing, and propose a fair packet forwarding strategy which operates as a plugin for traditional social- network-based routing protocols. My strategy improves the imbalance of success rates among users while maintaining approximately the same system throughput. DOI: 10.5353/th_b5089960 Subjects: Online social networks Data mining

Social Networking

Author : Mrutyunjaya Panda
Publisher : Springer
ISBN 13 : 3319051644
Total Pages : 313 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Social Networking by : Mrutyunjaya Panda

Download or read book Social Networking written by Mrutyunjaya Panda and published by Springer. This book was released on 2014-07-08 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the proliferation of social media and on-line communities in networked world a large gamut of data has been collected and stored in databases. The rate at which such data is stored is growing at a phenomenal rate and pushing the classical methods of data analysis to their limits. This book presents an integrated framework of recent empirical and theoretical research on social network analysis based on a wide range of techniques from various disciplines like data mining, social sciences, mathematics, statistics, physics, network science, machine learning with visualization techniques and security. The book illustrates the potential of multi-disciplinary techniques in various real life problems and intends to motivate researchers in social network analysis to design more effective tools by integrating swarm intelligence and data mining.

Encyclopedia of Social Network Analysis and Mining

Author : Reda Alhajj
Publisher : Springer
ISBN 13 : 9781493971305
Total Pages : 0 pages
Book Rating : 4.9/5 (713 download)

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Book Synopsis Encyclopedia of Social Network Analysis and Mining by : Reda Alhajj

Download or read book Encyclopedia of Social Network Analysis and Mining written by Reda Alhajj and published by Springer. This book was released on 2018-05-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Encyclopedia of Social Network Analysis and Mining (ESNAM) is the first major reference work to integrate fundamental concepts and research directions in the areas of social networks and applications to data mining. The second edition of ESNAM is a truly outstanding reference appealing to researchers, practitioners, instructors and students (both undergraduate and graduate), as well as the general public. This updated reference integrates all basics concepts and research efforts under one umbrella. Coverage has been expanded to include new emerging topics such as crowdsourcing, opinion mining, and sentiment analysis. Revised content of existing material keeps the encyclopedia current. The second edition is intended for college students as well as public and academic libraries. It is anticipated to continue to stimulate more awareness of social network applications and research efforts. The advent of electronic communication, and in particular on-line communities, have created social networks of hitherto unimaginable sizes. Reflecting the interdisciplinary nature of this unique field, the essential contributions of diverse disciplines, from computer science, mathematics, and statistics to sociology and behavioral science, are described among the 300 authoritative yet highly readable entries. Students will find a world of information and insight behind the familiar façade of the social networks in which they participate. Researchers and practitioners will benefit from a comprehensive perspective on the methodologies for analysis of constructed networks, and the data mining and machine learning techniques that have proved attractive for sophisticated knowledge discovery in complex applications. Also addressed is the application of social network methodologies to other domains, such as web networks and biological networks.

Machine Learning and Knowledge Discovery in Databases

Author : Peter A. Flach
Publisher : Springer
ISBN 13 : 9783642334856
Total Pages : 867 pages
Book Rating : 4.3/5 (348 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Peter A. Flach

Download or read book Machine Learning and Knowledge Discovery in Databases written by Peter A. Flach and published by Springer. This book was released on 2012-08-15 with total page 867 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.

Modeling Influence Diffusion in Networks for Community Detection, Resilience Analysis and Viral Marketing

Author : Wenjun Wang (Writer on information resources management)
Publisher :
ISBN 13 :
Total Pages : 163 pages
Book Rating : 4.:/5 (112 download)

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Book Synopsis Modeling Influence Diffusion in Networks for Community Detection, Resilience Analysis and Viral Marketing by : Wenjun Wang (Writer on information resources management)

Download or read book Modeling Influence Diffusion in Networks for Community Detection, Resilience Analysis and Viral Marketing written by Wenjun Wang (Writer on information resources management) and published by . This book was released on 2016 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decades have seen a fast-growing and dynamic trend of network science and its applications. From the Internet to Facebook, from telecommunications to power grids, from protein interactions to paper citations, networks are everywhere and the network paradigm is pervasive. Network analysis and mining has become an important tool for scientific research and industrial applications to diverse domains. For example, finding communities within social networks enables us to identify groups of densely connected customers who may share similar interests and behaviors and thus generate more effective recommender systems; investigating the supply-network topological structure and growth model improves the resilience of supply networks against disruptions; and modeling influence diffusion in social networks provides insights into viral marketing strategies. However, none of these tasks is trivial. In fact, community detection, resilience analysis, and influence-diffusion modeling are all important challenges in complex networks. My PhD research contributes to these endeavors by exploring the implicit knowledge of connectivity and proximity encoded in the network graph topology. Our research originated from an attempt to find communities in networks. After carefully examining real-life communities and the features and limitations of a set of widely-used centrality measures, we develop a simple but powerful reachability-based influence-diffusion model. Based upon this model, we propose a new influence centrality and a novel shared-influence-neighbor (SIN) similarity. The former differentiates the comprehensive influence significance more precisely, and the latter gives rise to a refined vertex-pair closeness metric. Then we develop an influence-guided spherical K-means (IGSK) algorithm for community detection. Further, we propose two novel influence-guided label propagation (IGLP) algorithms for finding hierarchical communities in complex networks.

Recent Trends in Information and Communication Technology

Author : Faisal Saeed
Publisher : Springer
ISBN 13 : 3319594273
Total Pages : 931 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Recent Trends in Information and Communication Technology by : Faisal Saeed

Download or read book Recent Trends in Information and Communication Technology written by Faisal Saeed and published by Springer. This book was released on 2017-05-24 with total page 931 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents 94 papers from the 2nd International Conference of Reliable Information and Communication Technology 2017 (IRICT 2017), held in Johor, Malaysia, on April 23–24, 2017. Focusing on the latest ICT innovations for data engineering, the book presents several hot research topics, including advances in big data analysis techniques and applications; mobile networks; applications and usability; reliable communication systems; advances in computer vision, artificial intelligence and soft computing; reliable health informatics and cloud computing environments, e-learning acceptance models, recent trends in knowledge management and software engineering; security issues in the cyber world; as well as society and information technology.

Optimal Social Influence

Author : Wen Xu
Publisher : Springer Nature
ISBN 13 : 303037775X
Total Pages : 129 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Optimal Social Influence by : Wen Xu

Download or read book Optimal Social Influence written by Wen Xu and published by Springer Nature. This book was released on 2020-01-29 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Influence and Behavior Analysis in Social Networks and Social Media

Author : Mehmet Kaya
Publisher : Springer
ISBN 13 : 3030025926
Total Pages : 238 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Influence and Behavior Analysis in Social Networks and Social Media by : Mehmet Kaya

Download or read book Influence and Behavior Analysis in Social Networks and Social Media written by Mehmet Kaya and published by Springer. This book was released on 2018-12-11 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely book focuses on influence and behavior analysis in the broader context of social network applications and social media. Twitter accounts of telecommunications companies are analyzed. Rumor sources in finite graphs with boundary effects by message-passing algorithms are identified. The coherent, state-of-the-art collection of chapters was initially selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. Original chapters coming from outside of the meeting round out the coverage. The result will appeal to researchers and students working in social network and social media analysis.

Web Technologies and Applications

Author : Weihong Han
Publisher : Springer
ISBN 13 : 3319111191
Total Pages : 414 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Web Technologies and Applications by : Weihong Han

Download or read book Web Technologies and Applications written by Weihong Han and published by Springer. This book was released on 2014-08-15 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the workshops held at the 16th Asia-Pacific Web Conference, APWeb 2014, in Changsha, China, in September 2014. The 34 full papers were carefully reviewed and selected from 59 submissions. This volume presents the papers that have been accepted for the following workshops: First International Workshop on Social Network Analysis, SNA 2014; First International Workshop on Network and Information Security, NIS 2014; First International Workshop on Internet of Things Search, IoTS 2014. The papers cover various issues in social network analysis, security and information retrieval against the heterogeneous big data.

Social Media Mining

Author : Reza Zafarani
Publisher : Cambridge University Press
ISBN 13 : 1107018854
Total Pages : 337 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Social Media Mining by : Reza Zafarani

Download or read book Social Media Mining written by Reza Zafarani and published by Cambridge University Press. This book was released on 2014-04-28 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining.

Networks, Crowds, and Markets

Author : David Easley
Publisher : Cambridge University Press
ISBN 13 : 1139490303
Total Pages : 745 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Networks, Crowds, and Markets by : David Easley

Download or read book Networks, Crowds, and Markets written by David Easley and published by Cambridge University Press. This book was released on 2010-07-19 with total page 745 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are all film stars linked to Kevin Bacon? Why do the stock markets rise and fall sharply on the strength of a vague rumour? How does gossip spread so quickly? Are we all related through six degrees of separation? There is a growing awareness of the complex networks that pervade modern society. We see them in the rapid growth of the internet, the ease of global communication, the swift spread of news and information, and in the way epidemics and financial crises develop with startling speed and intensity. This introductory book on the new science of networks takes an interdisciplinary approach, using economics, sociology, computing, information science and applied mathematics to address fundamental questions about the links that connect us, and the ways that our decisions can have consequences for others.