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Author: C. Lee Giles
Publisher: Springer Science & Business Media
Published: 2010-08-10
Total Pages: 141
ISBN-13: 3642149286
DOWNLOAD EBOOK →This work constitutes the proceedings of the Second International Workshop on Advances in Social Network and Analysis, held in Las Vegas, NV, USA in August 2008.
Author: Xu, Guandong
Publisher: IGI Global
Published: 2013-01-31
Total Pages: 272
ISBN-13: 1466628073
DOWNLOAD EBOOK →Social Media Mining and Social Network Analysis: Emerging Research highlights the advancements made in social network analysis and social web mining and its influence in the fields of computer science, information systems, sociology, organization science discipline and much more. This collection of perspectives on developmental practice is useful for industrial practitioners as well as researchers and scholars.
Author: Mehmet Kaya
Publisher: Springer
Published: 2017-03-21
Total Pages: 245
ISBN-13: 3319513672
DOWNLOAD EBOOK →This book presents the state-of-the-art in various aspects of analysis and mining of online social networks. Within the broader context of online social networks, it focuses on important and upcoming topics of social network analysis and mining such as the latest in sentiment trends research and a variety of techniques for community detection and analysis. The book collects chapters that are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2015), which was held in Paris, France in August 2015. All papers have been peer reviewed and checked carefully for overlap with the literature. The book will appeal to students and researchers in social network analysis/mining and machine learning.
Author: I-Hsien Ting
Publisher:
Published: 2012
Total Pages: 407
ISBN-13: 9781613505151
DOWNLOAD EBOOK →"This book covers current research trends in the area of social networks analysis and mining, sharing research from experts in the social network analysis and mining communities, as well as practitioners from social science, business, and computer science"--Provided by publisher.
Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
Published: 2011-03-18
Total Pages: 508
ISBN-13: 1441984623
DOWNLOAD EBOOK →Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.
Author: Reza Zafarani
Publisher: Cambridge University Press
Published: 2014-04-28
Total Pages: 337
ISBN-13: 1107018854
DOWNLOAD EBOOK →Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining.
Author: Reda Alhajj
Publisher: Springer
Published: 2018-05-02
Total Pages: 0
ISBN-13: 9781493971305
DOWNLOAD EBOOK →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.
Author: Deepayan Chakrabarti
Publisher: Morgan & Claypool Publishers
Published: 2012-10-01
Total Pages: 209
ISBN-13: 160845116X
DOWNLOAD EBOOK →What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions
Author: Ajith Abraham
Publisher: Springer
Published: 2012-03-01
Total Pages: 0
ISBN-13: 9781447125327
DOWNLOAD EBOOK →Social networks provide a powerful abstraction of the structure and dynamics of diverse kinds of people or people-to-technology interaction. Web 2.0 has enabled a new generation of web-based communities, social networks, and folksonomies to facilitate collaboration among different communities. This unique text/reference compares and contrasts the ethological approach to social behavior in animals with web-based evidence of social interaction, perceptual learning, information granulation, the behavior of humans and affinities between web-based social networks. An international team of leading experts present the latest advances of various topics in intelligent-social-networks and illustrates how organizations can gain competitive advantages by applying the different emergent techniques in real-world scenarios. The work incorporates experience reports, survey articles, and intelligence techniques and theories with specific network technology problems. Topics and Features: Provides an overview social network tools, and explores methods for discovering key players in social networks, designing self-organizing search systems, and clustering blog sites, surveys techniques for exploratory analysis and text mining of social networks, approaches to tracking online community interaction, and examines how the topological features of a system affects the flow of information, reviews the models of network evolution, covering scientific co-citation networks, nature-inspired frameworks, latent social networks in e-Learning systems, and compound communities, examines the relationship between the intent of web pages, their architecture and the communities who take part in their usage and creation, discusses team selection based on members’ social context, presents social network applications, including music recommendation and face recognition in photographs, explores the use of social networks in web services that focus on the discovery stage in the life cycle of these web services. This useful and comprehensive volume will be indispensible to senior undergraduate and postgraduate students taking courses in Social Intelligence, as well as to researchers, developers, and postgraduates interested in intelligent-social-networks research and related areas.
