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Crime, terrorism and security are in the forefront of current societal concerns. This edited volume presents research based on social network techniques showing how data from crime and terror networks can be analyzed and how information can be extracted. The topics covered include crime data mining and visualization; organized crime detection; crime network visualization; computational criminology; aspects of terror network analyses and threat prediction including cyberterrorism and the related area of dark web; privacy issues in social networks; security informatics; graph algorithms for social networks; general aspects of social networks such as pattern and anomaly detection; community discovery; link analysis and spatio-temporal network mining. These topics will be of interest to researchers and practitioners in the general area of security informatics.  The volume will also serve as a general reference for readers that would want to become familiar with current research in the fast growing field of cybersecurity.

Computer networks -- Security measures. --- Data mining. --- Online social networks. --- Terrorism --- Prevention. --- Anti-terrorism --- Antiterrorism --- Counter-terrorism --- Counterterrorism --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Physics. --- System safety. --- Complexity, Computational. --- Economic theory. --- Security Science and Technology. --- Data Mining and Knowledge Discovery. --- Criminology and Criminal Justice, general. --- Economic Theory/Quantitative Economics/Mathematical Methods. --- Complexity. --- Database searching --- Economic theory --- Political economy --- Social sciences --- Economic man --- Complexity, Computational --- Electronic data processing --- Machine theory --- Safety, System --- Safety of systems --- Systems safety --- Accidents --- Industrial safety --- Systems engineering --- Natural philosophy --- Philosophy, Natural --- Physical sciences --- Dynamics --- Prevention --- Criminology. --- Engineering. --- Crime --- Criminals --- Construction --- Industrial arts --- Technology --- Study and teaching --- Computational complexity.

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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. .

Social sciences. --- Social media. --- Data mining. --- Artificial intelligence. --- Social Sciences. --- Computational Social Sciences. --- Data Mining and Knowledge Discovery. --- Social Media. --- Artificial Intelligence (incl. Robotics). --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- User-generated media --- Communication --- User-generated content --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Online social networks --- Economic aspects. --- Electronic social networks --- Social networking Web sites --- Social media --- Social networks --- Sociotechnical systems --- Web sites --- Social sciences—Data processing. --- Social sciences—Computer programs. --- Artificial Intelligence.

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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.

Computer science. --- Data mining. --- Physics. --- Computational intelligence. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Computational Intelligence. --- Complex Networks. --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Natural philosophy --- Philosophy, Natural --- Physical sciences --- Dynamics --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Informatics --- Science --- Online social networks --- Data mining --- Design. --- Data processing. --- Electronic social networks --- Social networking Web sites --- Social media --- Social networks --- Sociotechnical systems --- Web sites --- Engineering. --- Applications of Graph Theory and Complex Networks. --- Construction --- Industrial arts --- Technology --- Virtual communities

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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.

Computer science. --- Data mining. --- User interfaces (Computer systems). --- Computers and civilization. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Applications of Graph Theory and Complex Networks. --- Computers and Society. --- User Interfaces and Human Computer Interaction. --- Social prediction. --- Social networks. --- Social media. --- User-generated media --- Networking, Social --- Networks, Social --- Social networking --- Social support systems --- Support systems, Social --- Prediction, Social --- Social forecasting --- Sociological prediction --- Civilization and computers --- Civilization --- Interfaces, User (Computer systems) --- Human-machine systems --- Human-computer interaction --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Informatics --- Science --- Communication --- User-generated content --- Interpersonal relations --- Cliques (Sociology) --- Microblogs --- Forecasting --- Sociology --- Social indicators --- Statistical methods --- Physics. --- Natural philosophy --- Philosophy, Natural --- Physical sciences --- Dynamics

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Information systems --- Computer. Automation --- IR (information retrieval) --- database management