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Machine Learning Techniques For Online Social Networks
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Book Synopsis Machine Learning Techniques for Online Social Networks by : Tansel Özyer
Download or read book Machine Learning Techniques for Online Social Networks written by Tansel Özyer and published by Springer. This book was released on 2018-05-30 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning in Social Networks by : Manasvi Aggarwal
Download or read book Machine Learning in Social Networks written by Manasvi Aggarwal and published by Springer Nature. This book was released on 2020-11-25 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein–protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area of current interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties.
Book Synopsis Hidden Link Prediction in Stochastic Social Networks by : Pandey, Babita
Download or read book Hidden Link Prediction in Stochastic Social Networks written by Pandey, Babita and published by IGI Global. This book was released on 2019-05-03 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers.
Book Synopsis User Protection in Social Networks Using Machine Learning Techniques by : Haoti Zhong
Download or read book User Protection in Social Networks Using Machine Learning Techniques written by Haoti Zhong and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Huge volumes of data, including images and text, are generated every day as a result of the booming of social networks and their corresponding users. Users share content including their personal information, and spend a lot of time on social networks. As an unexpected side effect, issues such as cyberbullying incidents or private-information leakage have seen a steep rise, and have proven harmful to certain users. Some of the negative effects appear ephemerally while others may have a long term impact on the users. For example, users may get their credit card stolen if they accidentally post a private photo containing such information. Therefore, some protection mechanisms are necessary with respect to these data. However, due to the high cost of manual detection of these harmful incidents, and possible failure for humans to make decisions accurately (some is hidden information or not easily recognizable), it is not feasible to expect human to complete these detection tasks. Machine learning algorithms are suitable to overcome these difficulties we just mentioned and help to provide a better user experience. In this thesis, we explore and design machine learning algorithms to solve three major problems appearing in online social network sites.
Book Synopsis Learning Automata Approach for Social Networks by : Alireza Rezvanian
Download or read book Learning Automata Approach for Social Networks written by Alireza Rezvanian and published by Springer. This book was released on 2019-01-22 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis. As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence.
Book Synopsis Understanding and Reshaping Social Networks with Advanced Computational Techniques by : Yuan Yuan (Expert on online social networks)
Download or read book Understanding and Reshaping Social Networks with Advanced Computational Techniques written by Yuan Yuan (Expert on online social networks) and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social networks are powerful in modeling interdependence among individuals. Recently, the availability of large-scale social network data and advances in computational tools have facilitated the rapid development in social network research. However, a few important aspects of social networks have been understudied, and advanced computational tools may not directly help social scientists draw scientific knowledge. My thesis thus aims to move towards applying and developing computational tools that help investigate important questions on social networks. The first component of my thesis focuses on understanding social interactions and networks, which offers implications for reshaping social networks to improve social cohesion. Specifically, I examine the formation and dynamics of social networks, with a focus on social exchange and "long ties." Utilizing large-scale social network data and computational tools, I first discuss benefits of the social exchange with dissimilar people in social networks; and then I proceed to study dynamic social networks and focus on long ties, or the social ties that bridge different communities in dynamic networks. Methodologically, I develop a novel interdisciplinary approach that combines game theory and machine learning techniques Second, I study what features on online platforms may improve social interactions and reshape social networks. To do so, I utilize large-scale data of online social media and provide two examples in the field. The first example is the identification of social contagion of online gift giving. This study examines how receiving a gift would promote the person to pay forward the gift, and also discusses how this social contagion can promote social interactions and tight social bonds. The other example is to examine how the designs of peer effects and prosociality on online social platforms encourage users' offline fitness behavior. Methodologically, both studies involve advanced causal inference and machine learning techniques to test the main hypotheses. Moreover, I develop computational tools that analyze social network data. In the final component of my thesis, I introduce an algorithm for controlled experiments in social networks. This algorithm detects heterogeneous spillover effects -- how the treatment assignments received by one's network neighbors affect a person's behavior -- in the data of networked experiments. This interdisciplinary algorithm combines approaches in causal inference, machine learning, and network science.
Book Synopsis The Influence of Technology on Social Network Analysis and Mining by : Tansel Özyer
Download or read book The Influence of Technology on Social Network Analysis and Mining written by Tansel Özyer and published by Springer Science & Business Media. This book was released on 2013-03-15 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of social networks was originated in social and business communities. In recent years, social network research has advanced significantly; the development of sophisticated techniques for Social Network Analysis and Mining (SNAM) has been highly influenced by the online social Web sites, email logs, phone logs and instant messaging systems, which are widely analyzed using graph theory and machine learning techniques. People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of SNAM in academia, politics, homeland security and business. This follows the pattern of known entities of our society that have evolved into networks in which actors are increasingly dependent on their structural embedding General areas of interest to the book include information science and mathematics, communication studies, business and organizational studies, sociology, psychology, anthropology, applied linguistics, biology and medicine.
Book Synopsis Social Network Forensics, Cyber Security, and Machine Learning by : P. Venkata Krishna
Download or read book Social Network Forensics, Cyber Security, and Machine Learning written by P. Venkata Krishna and published by Springer. This book was released on 2018-12-29 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the issues and challenges in Online Social Networks (OSNs). It highlights various aspects of OSNs consisting of novel social network strategies and the development of services using different computing models. Moreover, the book investigates how OSNs are impacted by cutting-edge innovations.
Book Synopsis Big Data Analytics by : Mrutyunjaya Panda
Download or read book Big Data Analytics written by Mrutyunjaya Panda and published by CRC Press. This book was released on 2018-12-12 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools. This reference work deals with social network aspects of big data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.
Book Synopsis Online Social Networks Security by : Brij B. Gupta
Download or read book Online Social Networks Security written by Brij B. Gupta and published by CRC Press. This book was released on 2021-02-25 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, virtual meeting technology has become a part of the everyday lives of more and more people, often with the help of global online social networks (OSNs). These help users to build both social and professional links on a worldwide scale. The sharing of information and opinions are important features of OSNs. Users can describe recent activities and interests, share photos, videos, applications, and much more. The use of OSNs has increased at a rapid rate. Google+, Facebook, Twitter, LinkedIn, Sina Weibo, VKontakte, and Mixi are all OSNs that have become the preferred way of communication for a vast number of daily active users. Users spend substantial amounts of time updating their information, communicating with other users, and browsing one another’s accounts. OSNs obliterate geographical distance and can breach economic barrier. This popularity has made OSNs a fascinating test bed for cyberattacks comprising Cross-Site Scripting, SQL injection, DDoS, phishing, spamming, fake profile, spammer, etc. OSNs security: Principles, Algorithm, Applications, and Perspectives describe various attacks, classifying them, explaining their consequences, and offering. It also highlights some key contributions related to the current defensive approaches. Moreover, it shows how machine-learning and deep-learning methods can mitigate attacks on OSNs. Different technological solutions that have been proposed are also discussed. The topics, methodologies, and outcomes included in this book will help readers learn the importance of incentives in any technical solution to handle attacks against OSNs. The best practices and guidelines will show how to implement various attack-mitigation methodologies.
Book Synopsis Social Networks with Rich Edge Semantics by : Quan Zheng
Download or read book Social Networks with Rich Edge Semantics written by Quan Zheng and published by CRC Press. This book was released on 2017-08-15 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets. Features Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks.
Book Synopsis Social Network Mining, Analysis, and Research Trends: Techniques and Applications by : Ting, I-Hsien
Download or read book Social Network Mining, Analysis, and Research Trends: Techniques and Applications written by Ting, I-Hsien and published by IGI Global. This book was released on 2011-12-31 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.
Book Synopsis Social Media Data Mining and Analytics by : Gabor Szabo
Download or read book Social Media Data Mining and Analytics written by Gabor Szabo and published by John Wiley & Sons. This book was released on 2018-09-19 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness the power of social media to predict customer behavior and improve sales Social media is the biggest source of Big Data. Because of this, 90% of Fortune 500 companies are investing in Big Data initiatives that will help them predict consumer behavior to produce better sales results. Social Media Data Mining and Analytics shows analysts how to use sophisticated techniques to mine social media data, obtaining the information they need to generate amazing results for their businesses. Social Media Data Mining and Analytics isn't just another book on the business case for social media. Rather, this book provides hands-on examples for applying state-of-the-art tools and technologies to mine social media - examples include Twitter, Wikipedia, Stack Exchange, LiveJournal, movie reviews, and other rich data sources. In it, you will learn: The four key characteristics of online services-users, social networks, actions, and content The full data discovery lifecycle-data extraction, storage, analysis, and visualization How to work with code and extract data to create solutions How to use Big Data to make accurate customer predictions How to personalize the social media experience using machine learning Using the techniques the authors detail will provide organizations the competitive advantage they need to harness the rich data available from social media platforms.
Book Synopsis From Social Data Mining and Analysis to Prediction and Community Detection by : Mehmet Kaya
Download or read book From Social Data Mining and Analysis to Prediction and Community Detection written by Mehmet Kaya and published by Springer. This book was released on 2017-03-21 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Social Networks: A Framework of Computational Intelligence by : Witold Pedrycz
Download or read book Social Networks: A Framework of Computational Intelligence written by Witold Pedrycz and published by Springer. This book was released on 2013-12-09 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides the audience with an updated, in-depth and highly coherent material on the conceptually appealing and practically sound information technology of Computational Intelligence applied to the analysis, synthesis and evaluation of social networks. The volume involves studies devoted to key issues of social networks including community structure detection in networks, online social networks, knowledge growth and evaluation, and diversity of collaboration mechanisms. The book engages a wealth of methods of Computational Intelligence along with well-known techniques of linear programming, Formal Concept Analysis, machine learning, and agent modeling. Human-centricity is of paramount relevance and this facet manifests in many ways including personalized semantics, trust metric, and personal knowledge management; just to highlight a few of these aspects. The contributors to this volume report on various essential applications including cyber attacks detection, building enterprise social networks, business intelligence and forming collaboration schemes. Given the subject area, this book is aimed at a broad audience of researchers and practitioners. Owing to the nature of the material being covered and a way it is organized, the volume will appeal to the well-established communities including those active in various disciplines in which social networks, their analysis and optimization are of genuine relevance. Those involved in operations research, management, various branches of engineering, and economics will benefit from the exposure to the subject matter.
Book Synopsis Detection of Suspicious URLs in Online Social Networks Using Supervised Machine Learning Algorithms by : Mohammed Fadhil Zamil Al-Janabi
Download or read book Detection of Suspicious URLs in Online Social Networks Using Supervised Machine Learning Algorithms written by Mohammed Fadhil Zamil Al-Janabi and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Data and Social Networks by : David Mohaisen
Download or read book Computational Data and Social Networks written by David Mohaisen and published by Springer Nature. This book was released on 2021-12-03 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Conference on Computational Data and Social Networks, CSoNet 2021, which was held online during November 15-17, 2021. The conference was initially planned to take place in Montreal, Quebec, Canada, but changed to an online event due to the COVID-19 pandemic. The 24 full and 8 short papers included in this book were carefully reviewed and selected from 57 submissions. They were organized in topical sections as follows: Combinatorial optimization and learning; deep learning and applications to complex and social systems; measurements of insight from data; complex networks analytics; special track on fact-checking, fake news and malware detection in online social networks; and special track on information spread in social and data networks.
