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Social media shatters the barrier to communicate anytime anywhere for people of all walks of life. The publicly available, virtually free information in social media poses a new challenge to consumers who have to discern whether a piece of information published in social media is reliable. For example, it can be difficult to understand the motivations behind a statement passed from one user to another, without knowing the person who originated the message. Additionally, false information can be propagated through social media, resulting in embarrassment or irreversible damages. Provenance data associated with a social media statement can help dispel rumors, clarify opinions, and confirm facts. However, provenance data about social media statements is not readily available to users today. Currently, providing this data to users requires changing the social media infrastructure or offering subscription services. Taking advantage of social media features, research in this nascent field spearheads the search for a way to provide provenance data to social media users, thus leveraging social media itself by mining it for the provenance data. Searching for provenance data reveals an interesting problem space requiring the development and application of new metrics in order to provide meaningful provenance data to social media users. This lecture reviews the current research on information provenance, explores exciting research opportunities to address pressing needs, and shows how data mining can enable a social media user to make informed judgements about statements published in social media.
Table of Contents: Information Provenance in Social Media / Provenance Attributes / Provenance via Network Information / Provenance Data

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Real world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge.


In this monograph, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from interconnected data. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including (1) rank-based clustering and classification, (2) meta-path-based similarity search and mining, (3) relation strength-aware mining, and many other potential developments. This monograph introduces this new research frontier and points out some promising research directions.

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What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others.
In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with «what if» scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous «pageRank» algorithm and the «HITS» algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints.
Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions

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This synthesis lecture provides a survey of work on privacy in online social networks (OSNs). This work encompasses concerns of users as well as service providers and third parties. Our goal is to approach such concerns from a computer-science perspective, and building upon existing work on privacy, security, statistical modeling and databases to provide an overview of the technical and algorithmic issues related to privacy in OSNs. We start our survey by introducing a simple OSN data model and describe common statistical-inference techniques that can be used to infer potentially sensitive information. Next, we describe some privacy definitions and privacy mechanisms for data publishing. Finally, we describe a set of recent techniques for modeling, evaluating, and managing individual users' privacy risk within the context of OSNs.
Table of Contents: Introduction / A Model for Online Social Networks / Types of Privacy Disclosure / Statistical Methods for Inferring Information in Networks / Anonymity and Differential Privacy / Attacks and Privacy-preserving Mechanisms / Models of Information Sharing / Users' Privacy Risk / Management of Privacy Settings

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Mobile platform development has lately become a technological war zone with extremely dynamic and fluid movement, especially in the smart phone and tablet market space. This Synthesis lecture is a guide to the latest developments of the key mobile platforms that are shaping the mobile platform industry. The book covers the three currently dominant native platforms – iOS, Android and Windows Phone – along with the device-agnostic HTML5 mobile web platform. The lecture also covers location-based services (LBS) which can be considered as a platform in its own right. The lecture utilizes a sample application (TwitterSearch) that the authors show programmed on each of the platforms.
Audiences who may benefit from this lecture include: (1) undergraduate and graduate students taking mobile computing classes or self-learning the mobile platform programmability road map; (2) academic and industrial researchers working on mobile computing R&D projects; (3) mobile app developers for a specific platform who may be curious about other platforms; (4) system integrator consultants and firms concerned with mobilizing businesses and enterprise apps; and (5) industries including health care, logistics, mobile workforce management, mobile commerce and payment systems and mobile search and advertisement.
Table of Contents: From the Newton to the iPhone / iOS / Android / Windows Phone / Mobile Web / Platform-in-Platform: Location-Based Services (LBS) / The Future of Mobile Platforms / TwitterSearch Sample Application

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The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of \emph{community detection and mining in social media}. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel.


This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information.


Table of Contents: Social Media and Social Computing / Nodes, Ties, and Influence / Community Detection and Evaluation / Communities in Heterogeneous Networks / Social Media Mining

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This book offers a comprehensive overview of the various concepts and research issues about blogs or weblogs. It introduces techniques and approaches, tools and applications, and evaluation methodologies with examples and case studies. Blogs allow people to express their thoughts, voice their opinions, and share their experiences and ideas. Blogs also facilitate interactions among individuals creating a network with unique characteristics. Through the interactions individuals experience a sense of community. We elaborate on approaches that extract communities and cluster blogs based on information of the bloggers. Open standards and low barrier to publication in Blogosphere have transformed information consumers to producers, generating an overwhelming amount of ever-increasing knowledge about the members, their environment and symbiosis. We elaborate on approaches that sift through humongous blog data sources to identify influential and trustworthy bloggers leveraging content and network information. Spam blogs or «splogs» are an increasing concern in Blogosphere and are discussed in detail with the approaches leveraging supervised machine learning algorithms and interaction patterns. We elaborate on data collection procedures, provide resources for blog data repositories, mention various visualization and analysis tools in Blogosphere, and explain conventional and novel evaluation methodologies, to help perform research in the Blogosphere.

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Лекция об истории, современном состоянии и предполагаемом будущем криптографии, и не только. С криптографией мы сталкиваемся чаще, чем замечаем это: каждая банковская транзакция, каждый разговор по мобильному телефону, не говоря уже о выходе в Интернет с настольного компьютера – всюду происходит обмен зашифрованной информацией. Как устроены используемые при этом алгоритмы? Какие есть у нас основания доверять им? Устойчивы ли эти основания и что мы будем делать, если (когда) эти основания будут разрушены? Лекция должна пригодиться тем, кто хочет лучше ориентироваться в этих вопросах. Александр Гуфан, доктор физико-математических наук, доцент, старший научный сотрудник отдела Кристаллофизики НИИ Физики Южного Федерального Университета рассказывает о том, какие существуют методы и системы защиты информации. Вы узнаете, как шифровал свою переписку Гай Юлий Цезарь и что таким способом шифруют сегодня, какие криптографические средства защиты информации используются чаще всего, как биотехнологическим корпорациям становится известно о пиратском размножении своих изделий и почему квантовые компьютеры испортят Интернет. наука, научпоп, физика, НИИФизики, информация, защитаинформации, криптография, шифрование, квантовыекомпьютеры

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Эта книга о карьере в информационной безопасности, о месте информационной безопасности в безопасности бизнеса и не только.Книга предназначена прежде всего для тех, кто начинает свой путь в информационной безопасности – обучается на соответствующих специальностях в средних и высших учебных заведениях.Книга может быть полезна руководителям и специалистам по работе с персоналом, т.к. в ней изложен опыт автора по адаптации работников в подразделении информационной безопасности.

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PHP – один из самых простых в освоении языков программирования. С помощью учебника PHP и правильной мотивации вы можете написать первые скрипты и выполнить команды в течение нескольких часов.

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