Social Network Data Analytics (eBook)

Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes.

Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book.

This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 140 papers in major conferences and journals in the database and data mining field. He has applied for or been granted over 75 US and International patents, and has thrice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 18 invention achievement awards by IBM for his patents. His work on real time stream analysis was recognized with the IBM Epispire award for environmental excellence in 2003, and the IBM Outstanding Technical Achievement award in 2010. His contributions to privacy technology were recognized with the IBM Outstanding Innovation Award in 2008. He has served on the program committee of most major data mining conferences, and was program vice-chairs for the WWW Conference, 2009, for the ICDM Conference and SIAM Data Mining Conference several times. He has also served as program chair for several workshops. He served as an associate editor of the IEEE Transactions on Data Engineering for two terms from 2004 to 2008. He is an associate editor of the ACM SIGKDD Explorations, an action editor of the Data Mining and Knowledge Discovery Journal, and an editorial board member of the Knowledge and Information Systems Journal. He is a fellow of the IEEE for contributions to knowledge discovery and data mining algorithms.

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Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.