This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services. Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'2016), which was held in August 2016. The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field.

Autorentext
Mehmet Kaya

He received the BSc. degree in Electrical and Electronics Engineering in 1996 and the MSc. and PhD. degrees in Computer Engineering in 1999 and 2003, respectively, all from Firat University, Elazig, Turkey. Currently, he is a Professor in the Department of Computer Engineering, Firat University. He spent 2002 as a Visiting Scholar at the ADSA Laboratory, Department of Computer Science, University of Calgary, Canada.

He has published over 80 papers in refereed international journals and conferences. His primary work and research interests are in the areas of data mining, social network analysis and mining, multi-agent systems, machine learning, and soft computing.

Jalal Kawash

He received the PhD degree in Computer Science in 2000 from University of Calgary. Currently, he is a Teaching Professor in the Department of Computer Science, University of Calgary. His primary work and research interests are in the areas of distributed systems, web and internet computing, and mobile virtual communities.

Suheil Khoury

Suheil Khoury has taught in mathematics departments for more than 16 years at institutions such as the College of Science and Technology, Jerusalem; the University of Houston-Downtown, USA; and Amman University, Jordan. His areas of research and teaching interest are differential equations, mathematical fluid dynamics, and computational and numerical mathematics. He won a teaching award from Michigan State University and the Shoman Prize in Mathematics.

Min-Yuh Day Dr. Min-Yuh Day is an Assistant Professor in the Department of Information Management at Tamkang University, Taiwan. Prior to joining the faculty at TKU in 2011, he was a Postdoctoral Fellow in the Intelligent Agent Systems Lab, Institute of Information Science, Academia Sinica, Taiwan. He received the Ph.D. degree from the Department of Information Management at National Taiwan University, Taiwan. He received his MBA in Management Information System from Tamkang University, Taiwan. His current research interests include Electronic Commerce, Financial Technology, Social Media Marketing, Information Systems Evaluation, Question Answering Systems, Data Mining and Text Mining, and Biomedical Informatics. He has published papers in Information & Management, Decision Support Systems, Integrated Computer-Aided Engineering, ACM Transactions on Asian Language Information Processing, and a number of international conference proceedings.

Inhalt

Chapter1. Twitter as a Source for Time and Domain Dependent Sentiment Lexicons.- Chapter2. Hiding in Plain Sight: The Anatomy of Malicious Pages on Facebook.- Chapter3. Extraction and Analysis of Dynamic Conversational Networks from TV Series.- Chapter4. Diversity and Influence as Key Measures to Assess Candidates for Hiring or Promotion in Academia.- Chapter5. Timelines of Prostate Cancer Biomarkers.- Chapter6. Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks.- Chapter7. Influence and Extension of the Spiral of Silence in Social Networks: A Data-driven Approach.- Chapter8. Prepaid or Postpaid? That is the question.\ Novel Methods of Subscription Type Prediction in Mobile Phone Services.- Chapter9. Dynamic Pattern Detection for Big Data Stream Analytics.- Chapter10. Community-based Recommendation for Cold-Start Problem: A Case Study of Reciprocal Online Dating Recommendation.- Chapter11. Combining Feature Extraction and Clustering for Better Face Recognition.