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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 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 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.
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 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.
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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