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This book focuses on recent technical advancements and state-of-the
art technologies for analyzing characteristic features and
probabilistic modelling of complex social networks and
decentralized online network architectures. Such research results
in applications related to surveillance and privacy, fraud
analysis, cyber forensics, propaganda campaigns, as well as for
online social networks such as Facebook. The text illustrates the
benefits of using advanced social network analysis methods through
application case studies based on practical test results from
synthetic and real-world data. This book will appeal to researchers
and students working in these areas.
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.
The book covers tools in the study of online social networks such
as machine learning techniques, clustering, and deep learning. A
variety of theoretical aspects, application domains, and case
studies for analyzing social network data are covered. The aim is
to provide new perspectives on utilizing machine learning and
related scientific methods and techniques for social network
analysis. Machine Learning Techniques for Online Social Networks
will appeal to researchers and students in these fields.
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.
Social network analysis increasingly bridges the discovery of
patterns in diverse areas of study as more data becomes available
and complex. Yet the construction of huge networks from large data
often requires entirely different approaches for analysis
including; graph theory, statistics, machine learning and data
mining. This work covers frontier studies on social network
analysis and mining from different perspectives such as social
network sites, financial data, e-mails, forums, academic research
funds, XML technology, blog content, community detection and clique
finding, prediction of user's- behavior, privacy in social network
analysis, mobility from spatio-temporal point of view, agent
technology and political parties in parliament. These topics will
be of interest to researchers and practitioners from different
disciplines including, but not limited to, social sciences and
engineering.
Social network analysis increasingly bridges the discovery of
patterns in diverse areas of study as more data becomes available
and complex. Yet the construction of huge networks from large data
often requires entirely different approaches for analysis
including; graph theory, statistics, machine learning and data
mining. This work covers frontier studies on social network
analysis and mining from different perspectives such as social
network sites, financial data, e-mails, forums, academic research
funds, XML technology, blog content, community detection and clique
finding, prediction of user's- behavior, privacy in social network
analysis, mobility from spatio-temporal point of view, agent
technology and political parties in parliament. These topics will
be of interest to researchers and practitioners from different
disciplines including, but not limited to, social sciences and
engineering.
The present text aims at helping the reader to maximize the reuse
of information. Topics covered include tools and services for
creating simple, rich, and reusable knowledge representations to
explore strategies for integrating this knowledge into legacy
systems. The reuse and integration are essential concepts that must
be enforced to avoid duplicating the effort and reinventing the
wheel each time in the same field. This problem is investigated
from different perspectives. in organizations, high volumes of data
from different sources form a big threat for filtering out the
information for effective decision making. the reader will be
informed of the most recent advances in information reuse and
integration.
The present work covers the latest developments and discoveries
related to information reuse and integration in academia and
industrial settings. The need for dealing with the large volumes of
data being produced and stored in the last decades and the numerous
systems developed to deal with these is increasingly necessary. Not
all these developments could have been achieved without the
investing large amounts of resources. Over time, new data sources
evolve and data integration continues to be an essential and vital
requirement. Furthermore, systems and products need to be revised
to adapt new technologies and needs. Instead of building these from
scratch, researchers in the academia and industry have realized the
benefits of reusing existing components that have been well tested.
While this trend avoids reinventing the wheel, it comes at the cost
of finding the optimum set of existing components to be utilized
and how they should be integrated together and with the new
non-existing components which are to be developed. These nontrivial
tasks have led to challenging research problems in the academia and
industry. These issues are addressed in this book, which is
intended to be a unique resource for researchers, developers and
practitioners.
The present text aims at helping the reader to maximize the reuse
of information. Topics covered include tools and services for
creating simple, rich, and reusable knowledge representations to
explore strategies for integrating this knowledge into legacy
systems. The reuse and integration are essential concepts that must
be enforced to avoid duplicating the effort and reinventing the
wheel each time in the same field. This problem is investigated
from different perspectives. in organizations, high volumes of data
from different sources form a big threat for filtering out the
information for effective decision making. the reader will be
informed of the most recent advances in information reuse and
integration.
This book focuses on recent technical advancements and state-of-the
art technologies for analyzing characteristic features and
probabilistic modelling of complex social networks and
decentralized online network architectures. Such research results
in applications related to surveillance and privacy, fraud
analysis, cyber forensics, propaganda campaigns, as well as for
online social networks such as Facebook. The text illustrates the
benefits of using advanced social network analysis methods through
application case studies based on practical test results from
synthetic and real-world data. This book will appeal to researchers
and students working in these areas.
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