BIG DATA OVER NETWORKS

• Part I. Mathematical Foundations:
1. Tensor models – solution methods and applications Shiqian Ma, Bo Jiang, Xiuzhen Huang and Shuzhong Zhang
2. Sparsity-aware distributed learning Symeon Chouvardas, Yannis Kopsinis and Sergios Theodoridis
3. Optimization algorithms for big data with application in wireless networks Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun and Zhi-Quan Luo
4. A unified distributed algorithm for non-cooperative games Jong-Shi Pang and Meisam Razaviyayn
• Part II. Big Data over Cyber Networks:
5. Big data analytics systems Ganesh Ananthanarayanan and Ishai Menache
6. Distributed big data storage in optical wireless networks Chen Gong, Zhengyuan Xu and Xiaodong Wang
7. Big data aware wireless communication – challenges and opportunities Suzhi Bi, Rui Zhang, Zhi Ding and Shuguang Cui
8. Big data processing for smart grid security Lanchao Liu, Zhu Han, H. Vincent Poor and Shuguang Cui
• Part III. Big Data over Social Networks:
9. Big data: a new perspective on cities Riccardo Gallotti, Thomas Louail, Rémi Louf and Marc Barthelemy
10. High dimensional network analytics: mapping topic networks in Twitter data during the Arab Spring Kathleen M. Carley, Wei Wei and Kenneth Joseph
11. Social influence analysis in the big data era – a review Jianping Cao, Dongliang Duan, Liuqing Yang, Qingpeng Zhang, Senzhang Wang and Feiyue Wang
• Part IV. Big Data over Biological Networks:
12. Inference of gene regulatory networks – validation and uncertainty Xiaoning Qian, Byung-Jun Yoon and Edward R Dougherty
13. Inference of gene networks associated with the host response to infectious disease Zhe Gan, Xin Yuan, Ricardo Henao, Ephraim L. Tsalik and Lawrence Carin
14. Gene-set-based inference of biological network topologies from big molecular profiling data Lipi Acharya and Dongxiao Zhu
15. Large scale correlation mining for biomolecular network discovery Alfred Hero and Bala Rajaratnam.

Utilising both key mathematical tools and state-of-the-art research results, this text explores the principles underpinning large-scale information processing over networks and examines the crucial interaction between big data and its associated communication, social and biological networks. Written by experts in the diverse fields of machine learning, optimisation, statistics, signal processing, networking, communications, sociology and biology, this book employs two complementary approaches: first analysing how the underlying network constrains the upper-layer of collaborative big data processing, and second, examining how big data processing may boost performance in various networks. Unifying the broad scope of the book is the rigorous mathematical treatment of the subjects, which is enriched by in-depth discussion of future directions and numerous open-ended problems that conclude each chapter. Readers will be able to master the fundamental principles for dealing with big data over large systems, making it essential reading for graduate students, scientific researchers and industry practitioners alike.

Features
• The first text to examine the interplay between big data and networks using a coherent and systematic approach
• Promotes interdisciplinary research across different fields using a common bridge through big data analytics
• Equips researchers and practitioners in related fields with the basic tools for dealing big data over large systems and a solid understanding of the current status of research and development

Authors
• Shuguang Cui, Texas A & M University
Shuguang (Robert) Cui is an Associate Professor at Texas A&M University. His is a Fellow of IEEE and was selected as a Highly Cited Researcher by Thomson Reuters in 2014.
• Alfred O. Hero, III, University of Michigan, Ann Arbor
Alfred O. Hero, III is R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor, with appointments in the Departments of Electrical Engineering and Computer Science, Biomedical Engineering and Statistics. He is a Fellow of the IEEE.
• Zhi-Quan Luo, University of Minnesota
Zhi-Quan (Tom) Luo is a Professor at the University of Minnesota. He has served as the Editor-in-Chief of IEEE Transactions on Signal Processing and is a Fellow of the IEEE, SIAM, and the Royal Society of Canada.
• José M. F. Moura, Carnegie Mellon University, Pennsylvania
José M. F. Moura is Philip L. and Marsha Dowd University Professor at Carnegie Mellon University, with appointments in the Departments of Electrical and Computer Engineering and, by courtesy, of Biomedical Engineering. He is a Fellow of IEEE and AAAS, a corresponding member of the Academy of Sciences of Portugal, and a member of the US NAE.