Community Detection and Influence Maximization in Online Social Networks

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Publisher :
ISBN 13 :
Total Pages : 272 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Community Detection and Influence Maximization in Online Social Networks by : Kanna Gharib Falahi

Download or read book Community Detection and Influence Maximization in Online Social Networks written by Kanna Gharib Falahi and published by . This book was released on 2014 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: The detecting and clustering of data and users into communities on the social web are important and complex issues in order to develop smart marketing models in changing and evolving social ecosystems. These marketing models are created by individual decision to purchase a product and are influenced by friends and acquaintances. This leads to novel marketing models, which view users as members of online social network communities, rather than the traditional view of marketing to individuals. This thesis starts by examining models that detect communities in online social networks. Then an enhanced approach to detect community which clusters similar nodes together is suggested. Social relationships play an important role in determining user behavior. For example, a user might purchase a product that his/her friend recently bought. Such a phenomenon is called social influence and is used to study how far the action of one user can affect the behaviors of others. Then an original metric used to compute the influential power of social network users based on logs of common actions in order to infer a probabilistic influence propagation model. Finally, a combined community detection algorithm and suggested influence propagation approach reveals a new influence maximization model by identifying and using the most influential users within their communities. In doing so, we employed a fuzzy logic based technique to determine the key users who drive this influence in their communities and diffuse a certain behavior. This original approach contrasts with previous influence propagation models, which did not use similarity opportunities among members of communities to maximize influence propagation. The performance results show that the model activates a higher number of overall nodes in contemporary social networks, starting from a smaller set of key users, as compared to existing landmark approaches which influence fewer nodes.

Social Network Analysis - Community Detection and Evolution

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Publisher : Springer
ISBN 13 : 331912188X
Total Pages : 282 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Social Network Analysis - Community Detection and Evolution by : Rokia Missaoui

Download or read book Social Network Analysis - Community Detection and Evolution written by Rokia Missaoui and published by Springer. This book was released on 2015-01-13 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edited work will appeal to researchers, practitioners and students interested in the latest developments of social network analysis.

Information and Influence Propagation in Social Networks

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Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627051163
Total Pages : 179 pages
Book Rating : 4.6/5 (27 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 Morgan & Claypool Publishers. This book was released on 2013-10-01 with total page 179 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.

Community Detection in Social Networks

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (984 download)

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Book Synopsis Community Detection in Social Networks by : Lei Cui

Download or read book Community Detection in Social Networks written by Lei Cui and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Community structure is one of the essential properties of social networks. That is, the users can be divided into groups within which the communications are dense while between which the communications are sparse. This modular structure can disclose important cues, especially in online social networks, metabolic interaction networks, WWW, wireless sensor network and viral marketing, and facilitate creation, representation and transfer of knowledge and influence. For lack of standard and exact definition of community structure of social networks, we take the factors of time complexity and effectiveness into account and design community detection algorithms adjusted to various application situations.\par In this dissertation, based on exiting popular community detection methods, several innovative methods and ideas were proposed, including Global Influence-based Maximum $K$-Community Partition (GI-MKCP), Degree-based Terminal-Set-Enhanced Community Detection (TSECD-D), Influence-based Modularity Maximization and Competitive Influence Maximization Game-based community detection. Due to the NP-hardness of general community detection problems, our proposed algorithms can be executed in polynomial time and approximate the optimal solution. More importantly, validated and demonstrated by the experiments performed on benchmark networks, the proposed algorithms are able to generate high-quality community structures and outperform existing algorithms.

Advances in Electronics, Communication and Computing

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Publisher : Springer
ISBN 13 : 9811047650
Total Pages : 808 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Advances in Electronics, Communication and Computing by : Akhtar Kalam

Download or read book Advances in Electronics, Communication and Computing written by Akhtar Kalam and published by Springer. This book was released on 2017-10-27 with total page 808 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a compilation of research work in the interdisciplinary areas of electronics, communication, and computing. This book is specifically targeted at students, research scholars and academicians. The book covers the different approaches and techniques for specific applications, such as particle-swarm optimization, Otsu’s function and harmony search optimization algorithm, triple gate silicon on insulator (SOI)MOSFET, micro-Raman and Fourier Transform Infrared Spectroscopy (FTIR) analysis, high-k dielectric gate oxide, spectrum sensing in cognitive radio, microstrip antenna, Ground-penetrating radar (GPR) with conducting surfaces, and digital image forgery detection. The contents of the book will be useful to academic and professional researchers alike.

Hybrid Intelligence for Social Networks

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Publisher : Springer
ISBN 13 : 3319651390
Total Pages : 333 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Hybrid Intelligence for Social Networks by : Hema Banati

Download or read book Hybrid Intelligence for Social Networks written by Hema Banati and published by Springer. This book was released on 2017-11-28 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains aspects of social networks, varying from development and application of new artificial intelligence and computational intelligence techniques for social networks to understanding the impact of social networks. Chapters 1 and 2 deal with the basic strategies towards social networks such as mining text from such networks and applying social network metrics using a hybrid approach; Chaps. 3 to 8 focus on the prime research areas in social networks: community detection, influence maximization and opinion mining. Chapter 9 to 13 concentrate on studying the impact and use of social networks in society, primarily in education, commerce, and crowd sourcing. The contributions provide a multidimensional approach, and the book will serve graduate students and researchers as a reference in computer science, electronics engineering, communications, and information technology.

Learning Automata Approach for Social Networks

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Publisher : Springer
ISBN 13 : 3030107671
Total Pages : 329 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Learning Automata Approach for Social Networks by : Alireza Rezvanian

Download or read book Learning Automata Approach for Social Networks written by Alireza Rezvanian and published by Springer. This book was released on 2019-01-22 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis. As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence.

Broad Learning Through Fusions

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Publisher : Springer
ISBN 13 : 3030125289
Total Pages : 419 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Broad Learning Through Fusions by : Jiawei Zhang

Download or read book Broad Learning Through Fusions written by Jiawei Zhang and published by Springer. This book was released on 2019-06-08 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.

Optimal Social Influence

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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.

Maximizing Influence Through Information-Overloaded Online Social Networks

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Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Maximizing Influence Through Information-Overloaded Online Social Networks by : Aaron R. Sun

Download or read book Maximizing Influence Through Information-Overloaded Online Social Networks written by Aaron R. Sun and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online social communities have become an important communication channel for people to share and discover information. Pieces of information spread within the community via the underlying social network, from one individual to another. However, with the unprecedented ease and low cost of communication provided by online systems, information overload emerges as a negative factor that potentially threatens the effectiveness of communication. As such, traditional single-message based diffusion models, such as Independent Cascade Model (ICM), are inadequate for describing the role of information overload. We then proposed an extended version of ICM (EICM) that explicitly takes the message multiplicity into account, after examining the message exchange patterns observed from a real online social community. We extensively evaluated this new diffusion model and compared with standard ICM in addressing one fundamental algorithmic problem related to viral marketing: How to select a set of network nodes/individuals to facilitate information diffusion and maximize influence? The evaluation results obtained from using both real and simulated data sets show that our approach results in node-selection heuristics outperforming well-studied notions of various centrality measures based on the ICM. The study and findings presented in this research have important managerial implications. In particular, from a viral marketing perspective, information overload effect should be recognized in order for campaign managers to build advantages in their influence maximization decisions.

Social Network Based Big Data Analysis and Applications

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Publisher : Springer
ISBN 13 : 3319781960
Total Pages : 254 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Social Network Based Big Data Analysis and Applications by : Mehmet Kaya

Download or read book Social Network Based Big Data Analysis and Applications written by Mehmet Kaya and published by Springer. This book was released on 2018-05-10 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services. Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2016), which was held in August 2016. The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field.

Web Information Systems Engineering

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Publisher : Springer Nature
ISBN 13 : 9811532818
Total Pages : 185 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Web Information Systems Engineering by : Leong Hou U

Download or read book Web Information Systems Engineering written by Leong Hou U and published by Springer Nature. This book was released on 2020-02-05 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings, presented on the 20th International Conference on Web Information Systems Engineering, WISE 2019 and on The International Workshop on Web Information Systems in the Era of AI, held in Hong Kong and Macau, China. Due to the problems in Hong Kong, WISE 2019 has been postponed until January 2020. The 7 workshop papers, 5 demo papers and 3 tutorial papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in the following sections: tutorials; demos; the International Workshop on Web Information Systems in the Era of AI.

Influence and Behavior Analysis in Social Networks and Social Media

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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.

Practical Social Network Analysis with Python

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Publisher : Springer
ISBN 13 : 3319967460
Total Pages : 355 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Practical Social Network Analysis with Python by : Krishna Raj P.M.

Download or read book Practical Social Network Analysis with Python written by Krishna Raj P.M. and published by Springer. This book was released on 2018-08-25 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on social network analysis from a computational perspective, introducing readers to the fundamental aspects of network theory by discussing the various metrics used to measure the social network. It covers different forms of graphs and their analysis using techniques like filtering, clustering and rule mining, as well as important theories like small world phenomenon. It also presents methods for identifying influential nodes in the network and information dissemination models. Further, it uses examples to explain the tools for visualising large-scale networks, and explores emerging topics like big data and deep learning in the context of social network analysis. With the Internet becoming part of our everyday lives, social networking tools are used as the primary means of communication. And as the volume and speed of such data is increasing rapidly, there is a need to apply computational techniques to interpret and understand it. Moreover, relationships in molecular structures, co-authors in scientific journals, and developers in a software community can also be understood better by visualising them as networks. This book brings together the theory and practice of social network analysis and includes mathematical concepts, computational techniques and examples from the real world to offer readers an overview of this domain.

Community detection and mining in social media

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Publisher : Springer Nature
ISBN 13 : 3031019008
Total Pages : 126 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Community detection and mining in social media by : Lei Tang

Download or read book Community detection and mining in social media written by Lei Tang and published by Springer Nature. This book was released on 2022-06-01 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of \emph{community detection and mining in social media}. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information. Table of Contents: Social Media and Social Computing / Nodes, Ties, and Influence / Community Detection and Evaluation / Communities in Heterogeneous Networks / Social Media Mining

Mining Lurkers in Online Social Networks

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Publisher : Springer
ISBN 13 : 3030002292
Total Pages : 95 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Mining Lurkers in Online Social Networks by : Andrea Tagarelli

Download or read book Mining Lurkers in Online Social Networks written by Andrea Tagarelli and published by Springer. This book was released on 2018-11-09 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief brings order to the wealth of research studies that contribute to shape our understanding of on-line social networks (OSNs) lurking phenomena. This brief also drives the development of computational approaches that can be effectively applied to answer questions related to lurking behaviors, as well as to the engagement of lurkers in OSNs. All large-scale online social networks (OSNs) are characterized by a participation inequality principle, i.e., the crowd of an OSN does not actively contribute, rather it takes on a silent role. Silent users are also referred to as lurkers, since they gain benefit from others' information without significantly giving back to the community. Nevertheless, lurkers acquire knowledge from the OSN, therefore a major goal is to encourage them to more actively participate. Lurking behavior analysis has been long studied in social science and human-computer interaction fields, but it has also matured over the last few years in social network analysis and mining. While the main target audience corresponds to computer, network, and web data scientists, this brief might also help increase the visibility of the topic by bridging different closely related research fields. Practitioners, researchers and students interested in social networks, web search, data mining, computational social science and human-computer interaction will also find this brief useful research material .

Proceedings of the 20th International Conference Companion on World Wide Web

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Publisher :
ISBN 13 :
Total Pages : 524 pages
Book Rating : 4.:/5 (112 download)

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Book Synopsis Proceedings of the 20th International Conference Companion on World Wide Web by : S. Sadagopan

Download or read book Proceedings of the 20th International Conference Companion on World Wide Web written by S. Sadagopan and published by . This book was released on 2011 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: