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This book focusses on recommendation, behavior, and anomaly, among
of social media analysis. First, recommendation is vital for a
variety of applications to narrow down the search space and to
better guide people towards educated and personalized alternatives.
In this context, the book covers supporting students, food venue,
friend and paper recommendation to demonstrate the power of social
media data analysis. Secondly, this book treats behavior analysis
and understanding as important for a variety of applications,
including inspiring behavior from discussion platforms, determining
user choices, detecting following patterns, crowd behavior modeling
for emergency evacuation, tracking community structure, etc. Third,
fraud and anomaly detection have been well tackled based on social
media analysis. This has is illustrated in this book by identifying
anomalous nodes in a network, chasing undetected fraud processes,
discovering hidden knowledge, detecting clickbait, etc. With this
wide coverage, the book forms a good source for practitioners and
researchers, including instructors and students.
This book focuses on novel and state-of-the-art scientific work in
the area of detection and prediction techniques using information
found generally in graphs and particularly in social networks.
Community detection techniques are presented in diverse contexts
and for different applications while prediction methods for
structured and unstructured data are applied to a variety of fields
such as financial systems, security forums, and social networks.
The rest of the book focuses on graph-based techniques for data
analysis such as graph clustering and edge sampling. The research
presented in this volume was 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. This
book will appeal to practitioners, researchers and students in the
field.
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.
This book addresses the challenges of social network and social
media analysis in terms of prediction and inference. The chapters
collected here tackle these issues by proposing new analysis
methods and by examining mining methods for the vast amount of
social content produced. Social Networks (SNs) have become an
integral part of our lives; they are used for leisure, business,
government, medical, educational purposes and have attracted
billions of users. The challenges that stem from this wide adoption
of SNs are vast. These include generating realistic social network
topologies, awareness of user activities, topic and trend
generation, estimation of user attributes from their social
content, and behavior detection. This text has applications to
widely used platforms such as Twitter and Facebook and appeals to
students, researchers, and professionals in the field.
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.
This book addresses the challenges of social network and social
media analysis in terms of prediction and inference. The chapters
collected here tackle these issues by proposing new analysis
methods and by examining mining methods for the vast amount of
social content produced. Social Networks (SNs) have become an
integral part of our lives; they are used for leisure, business,
government, medical, educational purposes and have attracted
billions of users. The challenges that stem from this wide adoption
of SNs are vast. These include generating realistic social network
topologies, awareness of user activities, topic and trend
generation, estimation of user attributes from their social
content, and behavior detection. This text has applications to
widely used platforms such as Twitter and Facebook and appeals to
students, researchers, and professionals in the field.
This edited volume addresses the vast challenges of adapting Online
Social Media (OSM) to developing research methods and applications.
The topics cover generating realistic social network topologies,
awareness of user activities, topic and trend generation,
estimation of user attributes from their social content, behavior
detection, mining social content for common trends, identifying and
ranking social content sources, building friend-comprehension
tools, and many others. Each of the ten chapters tackle one or more
of these issues by proposing new analysis methods or new
visualization techniques, or both, for famous OSM applications such
as Twitter and Facebook. This collection of contributed chapters
address these challenges. Online Social Media has become part of
the daily lives of hundreds of millions of users generating an
immense amount of 'social content'. Addressing the challenges that
stem from this wide adaptation of OSM is what makes this book a
valuable contribution to the field of social networks.
This book focusses on recommendation, behavior, and anomaly, among
of social media analysis. First, recommendation is vital for a
variety of applications to narrow down the search space and to
better guide people towards educated and personalized alternatives.
In this context, the book covers supporting students, food venue,
friend and paper recommendation to demonstrate the power of social
media data analysis. Secondly, this book treats behavior analysis
and understanding as important for a variety of applications,
including inspiring behavior from discussion platforms, determining
user choices, detecting following patterns, crowd behavior modeling
for emergency evacuation, tracking community structure, etc. Third,
fraud and anomaly detection have been well tackled based on social
media analysis. This has is illustrated in this book by identifying
anomalous nodes in a network, chasing undetected fraud processes,
discovering hidden knowledge, detecting clickbait, etc. With this
wide coverage, the book forms a good source for practitioners and
researchers, including instructors and students.
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