Lecture Notes in Social Networks – serie

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.

The contributors in this book share, exchange, and develop new concepts, ideas, principles, and methodologies in order to advance and deepen our understanding of social networks in the new generation of Information and Communication Technologies (ICT) enabled by Web 2.0, also referred to as social media, to help policy-making. This interdisciplinary work provides a platform for researchers, practitioners, and graduate students from sociology, behavioral science, computer science, psychology, cultural studies, information systems, operations research and communication to share, exchange, learn, and develop new concepts, ideas, principles, and methodologies. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining will be of interest to researchers, practitioners, and graduate students from the various disciplines listed above. The text facilitates the dissemination of investigations of the dynamics and structure of web based social networks. The book can be used as a reference text for advanced courses on Social Network Analysis, Sociology, Communication, Organization Theory, Cyber-anthropology, Cyber-diplomacy, and Information Technology and Justice.

This book addresses and examines the impacts of applications and services for data management and analysis, such as infrastructure, platforms, software, and business processes, on both academia and industry.

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 edited volume presents advances in modeling and computational analysis techniques related to networks and online communities. This book will appeal to students, professors, and researchers working in the fields of data science, computational social science, and social network analysis.

This book is a timely collection of chapters that present the state of the art within the analysis and application of big data.

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.

Social machines are a type of network connected by interactive digital devices made possible by the ubiquitous adoption of technologies such as the Internet, the smartphone, social media and the read/write World Wide Web, connecting people at scale to document situations, cooperate on tasks, exchange information, or even simply to play. Existing social processes may be scaled up, and new social processes enabled, to solve problems, augment reality, create new sources of value, and disrupt existing practice.This book considers what talents one would need to understand or build a social machine, describes the state of the art, and speculates on the future, from the perspective of the EPSRC project SOCIAM – The Theory and Practice of Social Machines. The aim is to develop a set of tools and techniques for investigating, constructing and facilitating social machines, to enable us to narrow down pragmatically what is becoming a wide space, by asking ‘when willit be valuable to use these methods on a sociotechnical system?’ The systems for which the use of these methods adds value are social machines in which there is rich person-to-person communication, and where a large proportion of the machine’s behaviour is constituted by human interaction.

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 related to the educational networking (EN) domain, an incipient but disrupting trend engaged in extending and improving formal and informal academic practices by means of the support given by online social networks (OSNs) and Web 2.0 technologies. With the aim of contributing to spread the knowledge and development of the arena, this volume introduces ten recent works, whose content meets the quality criteria of formal scientific labor that is worthy to be published according to following five categories:·         Reviews: gather three overviews that focus on K-12 EN practice, mixed methods approaches using social network analysis for learning and education, and a broad landscape of the recent accomplished labor.·         Conceptual: presents a work where a theoretical framework is proposed to overcome barriers that constrain the use of OSNs for educational purposes bymeans of a Platform Adoption Model. ·         Projects: inform a couple of initiatives, where one fosters groups and networks for teachers involved in distance education, and the other encourages students the author academic videos to improve motivation and engagement.·         Approaches: offer three experiences related to: Wiki and Blog usage for assessment affairs, application of a method that encourages OSNs users to actively post and repost valuable information for the learning community, and the recreation of learning spaces in context–aware to boost EN.·         Study: applies an own method to ranking Mexican universities based on maximal clique, giving as a result a series of complex visual networks that characterize the tides among diverse features that describe academic institutions practice.In resume, this volumeoffers a fresh reference of an emergent field that contributes to spreading and enhancing the provision of education in classrooms and online settings through social constructivism and collaboration policy. Thus, it is expected the published content encourages researchers, practitioners, professors, and postgraduate students to consider their future contribution to extent the scope and impact of EN in formal and informal teaching and learning endeavors.

This book is related to the educational networking (EN) domain, an incipient but disrupting trend engaged in extending and improving formal and informal academic practices by means of the support given by online social networks (OSNs) and Web 2.0 technologies. With the aim of contributing to spread the knowledge and development of the arena, this volume introduces ten recent works, whose content meets the quality criteria of formal scientific labor that is worthy to be published according to following five categories:·         Reviews: gather three overviews that focus on K-12 EN practice, mixed methods approaches using social network analysis for learning and education, and a broad landscape of the recent accomplished labor.·         Conceptual: presents a work where a theoretical framework is proposed to overcome barriers that constrain the use of OSNs for educational purposes bymeans of a Platform Adoption Model. ·         Projects: inform a couple of initiatives, where one fosters groups and networks for teachers involved in distance education, and the other encourages students the author academic videos to improve motivation and engagement.·         Approaches: offer three experiences related to: Wiki and Blog usage for assessment affairs, application of a method that encourages OSNs users to actively post and repost valuable information for the learning community, and the recreation of learning spaces in context–aware to boost EN.·         Study: applies an own method to ranking Mexican universities based on maximal clique, giving as a result a series of complex visual networks that characterize the tides among diverse features that describe academic institutions practice.In resume, this volumeoffers a fresh reference of an emergent field that contributes to spreading and enhancing the provision of education in classrooms and online settings through social constructivism and collaboration policy. Thus, it is expected the published content encourages researchers, practitioners, professors, and postgraduate students to consider their future contribution to extent the scope and impact of EN in formal and informal teaching and learning endeavors.

Some authors explore new trends related to network measures, multilevel networks and clustering on networks, while other contributions deepen the relationship among statistical methods for data mining and social network analysis.

Some authors explore new trends related to network measures, multilevel networks and clustering on networks, while other contributions deepen the relationship among statistical methods for data mining and social network analysis.

This book focusses on recommendation, behavior, and anomaly, among of social media analysis.

This book focusses on recommendation, behavior, and anomaly, among of social media analysis.

The book explores Applied Phronesis in internet communication technology and Netnography application, introducing it on Facebook and YouTube usages. It defines two pillars for the research dynamics, “Episteme” and “Techne.” – the know-how, how-to, and the power dynamics. The “Episteme” explores the dynamics of reconciliation in the middle of conflict, Internet communication technologies for transformation, Moderation in Islam, online Deliberative Democracy.The second pillar, “Techne,” is explored through Internet communication technology for the advancement of reconciliation in the middle of a conflict.The book describes the Phronetic Approach in internet research in academic discourse adopting Phronesis “an Aristotelian concept and method defined by Bent Flyvbjerg,” and exploring Netnography for Kozinets, in Mixed-Method research design and applying methodological triangulation in research and testing the hypothesis using qualitative content analysis for Krippendorff, developing a methodological discourse for interdisciplinary research using internet communication technologies as part of understanding big-date, introducing Applied Digital Humanities.

The book explores Applied Phronesis in internet communication technology and Netnography application, introducing it on Facebook and YouTube usages. It defines two pillars for the research dynamics, “Episteme” and “Techne.” – the know-how, how-to, and the power dynamics. The “Episteme” explores the dynamics of reconciliation in the middle of conflict, Internet communication technologies for transformation, Moderation in Islam, online Deliberative Democracy.The second pillar, “Techne,” is explored through Internet communication technology for the advancement of reconciliation in the middle of a conflict.The book describes the Phronetic Approach in internet research in academic discourse adopting Phronesis “an Aristotelian concept and method defined by Bent Flyvbjerg,” and exploring Netnography for Kozinets, in Mixed-Method research design and applying methodological triangulation in research and testing the hypothesis using qualitative content analysis for Krippendorff, developing a methodological discourse for interdisciplinary research using internet communication technologies as part of understanding big-date, introducing Applied Digital Humanities.

This book shows how open source intelligence can be a powerful tool for combating crime by linking local and global patterns to help understand how criminal activities are connected. Readers will encounter the latest advances in cutting-edge data mining, machine learning and predictive analytics combined with natural language processing and social network analysis to detect, disrupt, and neutralize cyber and physical threats. Chapters contain state-of-the-art social media analytics and open source intelligence research trends. This multidisciplinary volume will appeal to students, researchers, and professionals working in the fields of open source intelligence, cyber crime and social network analytics.Chapter Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book shows how open source intelligence can be a powerful tool for combating crime by linking local and global patterns to help understand how criminal activities are connected. Readers will encounter the latest advances in cutting-edge data mining, machine learning and predictive analytics combined with natural language processing and social network analysis to detect, disrupt, and neutralize cyber and physical threats. Chapters contain state-of-the-art social media analytics and open source intelligence research trends. This multidisciplinary volume will appeal to students, researchers, and professionals working in the fields of open source intelligence, cyber crime and social network analytics.Chapter Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly identify new research problems in their domains.

This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly identify new research problems in their domains.

This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions from academic as well as industrial researchers active within this emerging new field.Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers’ social behavior can be avery useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfits. Another well known difficulty with recommendation of similar items is the large quantities of clothing items which can be considered similar, but belong to different brands. Relying only on implicit customer behavioral data will not be sufficient in the coming future to distinguish between for recommendation that will lead to an item being purchased and kept, vs. a recommendation that might result in either the customer not following it, or eventually return the item. Finding the right size and fit for clothes is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Moreover, fashion articles have important sizing variations. Finally, customer preferences towards perceived article size and fit for their body remain highly personal and subjective which influences the definition of the right size for each customer.The combination of the above factors leaves the customers alone to face a highly challenging problem of determining the right size and fit during their purchase journey, which in turn has resulted in having more than one third of apparel returns to be caused by not ordering the right article size. This challenge presents a huge opportunity for research in intelligent size and fit recommendation systems and machine learning solutions with direct impact on both customer satisfaction and business profitability.

This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions from academic as well as industrial researchers active within this emerging new field.Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers’ social behavior can be avery useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfits. Another well known difficulty with recommendation of similar items is the large quantities of clothing items which can be considered similar, but belong to different brands. Relying only on implicit customer behavioral data will not be sufficient in the coming future to distinguish between for recommendation that will lead to an item being purchased and kept, vs. a recommendation that might result in either the customer not following it, or eventually return the item. Finding the right size and fit for clothes is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Moreover, fashion articles have important sizing variations. Finally, customer preferences towards perceived article size and fit for their body remain highly personal and subjective which influences the definition of the right size for each customer.The combination of the above factors leaves the customers alone to face a highly challenging problem of determining the right size and fit during their purchase journey, which in turn has resulted in having more than one third of apparel returns to be caused by not ordering the right article size. This challenge presents a huge opportunity for research in intelligent size and fit recommendation systems and machine learning solutions with direct impact on both customer satisfaction and business profitability.

This edited book provides techniques which address various aspects of big data collection and analysis from social media platforms and beyond. It covers efficient compression of large networks, link prediction in hashtag graphs, visual exploration of social media data, identifying motifs in multivariate data, social media surveillance to enhance search and rescue missions, recommenders for collaborative filtering and safe travel plans to high risk destinations, analysis of cyber influence campaigns on YouTube, impact of location on business rating, bibliographical and co-authorship network analysis, and blog data analytics. All these trending topics form a major part of the state of the art in social media and big data analytics. Thus, this edited book may be considered as a valuable source for readers interested in grasping some of the most recent advancements in this high trending domain.

This edited book provides techniques which address various aspects of big data collection and analysis from social media platforms and beyond. It covers efficient compression of large networks, link prediction in hashtag graphs, visual exploration of social media data, identifying motifs in multivariate data, social media surveillance to enhance search and rescue missions, recommenders for collaborative filtering and safe travel plans to high risk destinations, analysis of cyber influence campaigns on YouTube, impact of location on business rating, bibliographical and co-authorship network analysis, and blog data analytics. All these trending topics form a major part of the state of the art in social media and big data analytics. Thus, this edited book may be considered as a valuable source for readers interested in grasping some of the most recent advancements in this high trending domain.

This book examines modern paradigms of disease control based on social network surveillance applications, including electronic sentinel surveillance and wireless application-based surveillance science.

This book examines modern paradigms of disease control based on social network surveillance applications, including electronic sentinel surveillance and wireless application-based surveillance science.

This book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning.

This book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning.