Community detection and mining in social media by Lei Tang
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-
Deep Learning for Polymer Discovery
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Provenance Data in Social Media
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Mining Structures of Factual Knowledge from Text
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Exploratory Causal Analysis with Time Series Data
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Probabilistic Approaches to Recommendations
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Detecting Fake News on Social Media
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Phrase Mining from Massive Text and Its Applications
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Modeling and Data Mining in Blogosphere
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Outlier Detection for Temporal Data
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Privacy in Social Networks
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Correlation Clustering
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Exploiting the Power of Group Differences
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Multidimensional Mining of Massive Text Data
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Individual and Collective Graph Mining
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Automated Taxonomy Discovery and Exploration
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Graph Mining
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Advances in Graph Neural Networks
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Mining Human Mobility in Location-Based Social Networks
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Ensemble Methods in Data Mining
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Mining Heterogeneous Information Networks
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Mining Latent Entity Structures
Dr. Guozhu Dong is a professor of Computer Science and Engineering at Wright State University. He obtained his Ph.D. in Computer Science from University of Southern California and his B.S. in Mathematics from Shandong University. Before joining Wright State University, he was a faculty member at Flinders University and then at the University of Melbourne. At Wright State University, he was recognized for Excellence in Research in the College of Engineering and Computer Science. His research interests are in data mining, machine learning, database, data science, and artificial intelligence. He co-authored a book on Sequence Data Mining and co-edited a book on Contrast Data Mining. He has served on numerous conference program committees.
Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in Computer Science and Electrical Engineering at Shanghai JiaoTong University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at National University of Singapore. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 President's Award for Innovation. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating interdisciplinary problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He is a co-author of Social Media Mining: An Introduction by Cambridge University Press. He serves on journal editorial boards and numerous conference program committees, and is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He is an IEEE Fellow. More can be found at http: //www.public.asu.edu/ huanliu.
| SKU | Unavailable |
| ISBN 13 | 9783031007729 |
| ISBN 10 | 3031007727 |
| Title | Community detection and mining in social media |
| Author | Lei Tang |
| Series | Synthesis Lectures On Data Mining And Knowledge Discovery |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2010-09-09 |
| Number of pages | 123 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |