Mining The Digital Information Networks

Electronic publishing is continuously changing; new technologies open new ways for individuals, scholars, communities and networks to establish contacts, exchange data, produce information and share knowledge on a variety of devices, from personal computers to mobile media. There is an urgent need to rethink electronic publishing in order to develop and use new communication paradigms and technologies, and to devise a truly digital format for the future. This book presents the conference proceedings of the ELPUB 2013 conference, held in Karlskrona, Sweden, in June 2013. The main theme of the conference is extracting and processing data from the vast wealth of digital publishing, and the ways to use and reuse this information in innovative social contexts in a sustainable way. The conference brings together researchers and practitioners to discuss data mining, digital publishing and social networks, along with their implications for scholarly communication, information services, e-learning, e-businesses, the cultural heritage sector and other areas where electronic publishing is imperative. The book is divided into three sections: full research articles, full professional articles and extended abstracts. Each section is further subdivided into Data Mining and Intelligent Computing, Publishing and Access and Social Computing and Practices. Focusing on key issues surrounding the development of methods for gathering and processing information, and on the means for making these data useful and accessible, this book will be of interest to the whole digital community.

Social Media Mining and Social Network Analysis: Emerging Research highlights the advancements made in social network analysis and social web mining and its influence in the fields of computer science, information systems, sociology, organization science discipline and much more. This collection of perspectives on developmental practice is useful for industrial practitioners as well as researchers and scholars.

Investigates the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network.

The ways in which research data is used and handled continue to capture public attention and are the focus of increasing interest. Electronic publishing is intrinsic to digital data management, and relevant to the fields of data mining, digital publishing and social networks, with their implications for scholarly communication, information services, e-learning, e-business and the cultural heritage sector._x000D_ This book presents the proceedings of the 18th International Conference on Electronic Publishing (ELPUB), held in Thessaloniki, Greece, in June 2014. The conference brings together researchers and practitioners to discuss the many aspects of electronic publishing, and the theme this year is 'Let's put data to use: digital scholarship for the next generation'. As well as examining the role of cultural heritage and service organisations in the creation, accessibility, duration and long-term preservation of data, it provides a discussion forum for the appraisal, citation and licensing of research data and the new developments in reviewing, publishing and editorial technology._x000D_ The book is divided into sections covering the following topics: open access and open data; knowing the users better; researchers and their needs; specialized content for researchers; publishing and access; and practical aspects of electronic publishing._x000D_ Providing an overview of all that is current in the electronic publishing world, this book will be of interest to practitioners, researchers and students in information science, as well as users of electronic publishing.

This comprehensive textbook/reference presents a focused review of the state of the art in privacy research, encompassing a range of diverse topics. The first book of its kind designed specifically to cater to courses on privacy, this authoritative volume provides technical, legal, and ethical perspectives on privacy issues from a global selection of renowned experts. Features: examines privacy issues relating to databases, P2P networks, big data technologies, social networks, and digital information networks; describes the challenges of addressing privacy concerns in various areas; reviews topics of privacy in electronic health systems, smart grid technology, vehicular ad-hoc networks, mobile devices, location-based systems, and crowdsourcing platforms; investigates approaches for protecting privacy in cloud applications; discusses the regulation of personal information disclosure and the privacy of individuals; presents the tools and the evidence to better understand consumers’ privacy behaviors.

Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.

This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of things (IoT), and information security.

The "big data" era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media, news, and everyone's daily life. Examples of such collections include scientific publications, enterprise logs, news articles, social media, and general web pages. Valuable knowledge about multi-typed entities is often hidden in the unstructured or loosely structured, interconnected data. Mining latent structures around entities uncovers hidden knowledge such as implicit topics, phrases, entity roles and relationships. In this monograph, we investigate the principles and methodologies of mining latent entity structures from massive unstructured and interconnected data. We propose a text-rich information network model for modeling data in many different domains. This leads to a series of new principles and powerful methodologies for mining latent structures, including (1) latent topical hierarchy, (2) quality topical phrases, (3) entity roles in hierarchical topical communities, and (4) entity relations. This book also introduces applications enabled by the mined structures and points out some promising research directions.

Online social networking sites like Facebook, LinkedIn, and Twitter, offer millions of members the opportunity to befriend one another, send messages to each other, and post content on the site — actions which generate mind-boggling amounts of data every day.To make sense of the massive data from these sites, we resort to social media mining to answer questions like the following: