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Big Data over Networks

Book Description

Contests are prevalent in many areas, including sports, rent seeking, patent races, innovation inducement, labor markets, scientific projects, crowdsourcing and other online services, and allocation of computer system resources. This book provides unified, comprehensive coverage of contest theory as developed in economics, computer science, and statistics, with a focus on online services applications, allowing professionals, researchers and students to learn about the underlying theoretical principles and to test them in practice. The book sets contest design in a game-theoretic framework that can be used to model a wide-range of problems and efficiency measures such as total and individual output and social welfare, and offers insight into how the structure of prizes relates to desired contest design objectives. Methods for rating the skills and ranking of players are presented, as are proportional allocation and similar allocation mechanisms, simultaneous contests, sharing utility of productive activities, sequential contests, and tournaments.

Table of Contents

  1. Cover
  2. Half title
  3. Title
  4. Copyright
  5. Table of Contents
  6. List of contributors
  7. Preface
  8. Part I Mathematical foundations
    1. 1 Tensor models: solution methods and applications
      1. 1.1 Introduction
      2. 1.2 Tensor models
        1. 1.2.1 Sparse and low-rank tensor optimization models
        2. 1.2.2 Tensor principal component analysis
        3. 1.2.3 The tensor co-clustering problem
      3. 1.3 Reformulation of tensor models
        1. 1.3.1 Low-n-rank tensor optimization
        2. 1.3.2 Equivalent formulation of tensor PCA
      4. 1.4 Solution methods
        1. 1.4.1 Directly resorting to some existing solver
        2. 1.4.2 First-order methods
        3. 1.4.3 The block optimization technique
      5. 1.5 Applications
        1. 1.5.1 Computational results on gene expression data
      6. 1.6 Conclusions
      7. References
    2. 2 Sparsity-aware distributed learning
      1. 2.1 Introduction
      2. 2.2 Batch distributed sparsity promoting algorithms
        1. 2.2.1 Problem formulation
        2. 2.2.2 LASSO and its distributed learning formulation
        3. 2.2.3 Sparsity-aware learning: the greedy point of view
        4. 2.2.4 Other distributed sparse recovery algorithms
      3. 2.3 Online sparsity-aware distributed learning
        1. 2.3.1 Problem description
        2. 2.3.2 LMS based sparsity-promoting algorithm
        3. 2.3.3 The GreeDi LMS algorithm
        4. 2.3.4 Set-theoretic sparsity-aware distributed learning
      4. 2.4 Simulation examples
        1. 2.4.1 Performance evaluation of batch methods
        2. 2.4.2 Performance evaluation of online methods
      5. References
    3. 3 Optimization algorithms for big data with application in wireless networks
      1. 3.1 Introduction
        1. 3.1.1 Motivation
        2. 3.1.2 The organization of the chapter
      2. 3.2 First-order algorithms for big data
        1. 3.2.1 The block coordinate descent algorithm
        2. 3.2.2 The ADMM algorithm
        3. 3.2.3 The BSUM method
      3. 3.3 Application to network provisioning problem
        1. 3.3.1 The setting
        2. 3.3.2 Network with an uncapacitated backhaul
        3. 3.3.3 Network with a capacitated backhaul
      4. 3.4 Numerical results
        1. 3.4.1 Scenario 1: Performance comparison with heuristic algorithms
        2. 3.4.2 Scenario 2: The efficiency of N-MaxMin WMMSE algorithm
        3. 3.4.3 Scenario 3: Multi-commodity routing problem with parallel mplementation
        4. 3.4.4 Scenario 4: Performance evaluation for Algorithm 1 with zones of nodes
      5. 3.5 Appendix
      6. References
    4. 4 A unified distributed algorithm for non-cooperative games
      1. 4.1 Introduction
      2. 4.2 The nonsmooth, nonconvex game
      3. 4.3 The unified algorithm
        1. 4.3.1 Special cases
      4. 4.4 Convergence analysis: contraction approach
        1. 4.4.1 Probabilistic player choices
      5. 4.5 Convergence analysis: potential approach
        1. 4.5.1 Generalized potential games
      6. References
      7. Appendix
  9. Part II Big data over cyber networks
    1. 5 Big data analytics systems
      1. 5.1 Introduction
      2. 5.2 Scheduling
        1. 5.2.1 Fairness
        2. 5.2.2 Placement constraints
        3. 5.2.3 Additional system-wide objectives
        4. 5.2.4 Stragglers
      3. 5.3 Storage
        1. 5.3.1 Distributed file system
        2. 5.3.2 In-memory storage
      4. 5.4 Concluding remarks
      5. References
    2. 6 Distributed big data storage in optical wireless networks
      1. 6.1 Introduction
      2. 6.2 Big data distributed storage in a wireless network
        1. 6.2.1 Wireless distributed storage network framework
        2. 6.2.2 Optical wireless framework
        3. 6.2.3 Rateless coded distributed data storage
        4. 6.2.4 Network coded system with full downloading
        5. 6.2.5 Network coded system with partial downloading
      3. 6.3 Reconstructability condition for partial downloading
        1. 6.3.1 μ-Reconstructability for MSR point
        2. 6.3.2 μ-Reconstructability for a practical MBR point coding scheme
      4. 6.4 Channel and power allocation for partial downloading
        1. 6.4.1 Wireless resource allocation framework
        2. 6.4.2 Optimal channel and power allocation for the relaxed problem
      5. 6.5 Open research topics
        1. 6.5.1 General research topics for wireless distributed storage networks
        2. 6.5.2 Research topics for data storage in optical wireless networks
        3. 6.5.3 Research topics for data storage in named data networks
      6. References
    3. 7 Big data aware wireless communication: challenges and opportunities
      1. 7.1 Introduction
      2. 7.2 Scalable wireless network architecture for big data
        1. 7.2.1 Hybrid processing structure
        2. 7.2.2 Web caching in wireless infrastructure
        3. 7.2.3 Data aware processing units
      3. 7.3 Wireless system design in big data era
        1. 7.3.1 Analog vs. digital backhaul
        2. 7.3.2 Joint base station and cloud processing with digital backhaul
        3. 7.3.3 Section summary
      4. 7.4 Big data aware wireless networking
        1. 7.4.1 Wireless big data analytics
        2. 7.4.2 Data-driven mobile cloud computing
        3. 7.4.3 Software-defined networking design
        4. 7.4.4 Section summary
      5. 7.5 Conclusions
      6. Acknowledgement
      7. References
    4. 8 Big data processing for smart grid security
      1. 8.1 Preliminaries and motivations
      2. 8.2 Sparse optimization for false data injection detection
        1. 8.2.1 State estimation and false data injection attacks
        2. 8.2.2 Nuclear norm minimization
        3. 8.2.3 Low-rank matrix factorization
        4. 8.2.4 Numerical results
      3. 8.3 Distributed approach for security-constrained optimal power flow
        1. 8.3.1 Security-constrained optimal power flow
        2. 8.3.2 ADMM method
        3. 8.3.3 Distributed and parallel approach for SCOPF
        4. 8.3.4 Numerical results
      4. 8.4 Concluding remarks
      5. Acknowledgement
      6. References
  10. Part III Big data over social networks
    1. 9 Big data: a new perspective on cities
      1. 9.1 Big data and urban systems
      2. 9.2 Infrastructure networks
        1. 9.2.1 Road networks
        2. 9.2.2 Subway networks
      3. 9.3 Mobility networks
        1. 9.3.1 A renewed interest
        2. 9.3.2 Individual mobility networks
        3. 9.3.3 From big data to the spatial structure of cities
      4. 9.4 Scaling in cities
      5. 9.5 Discussion: towards a new science of cities
      6. Acknowledgments
      7. References
    2. 10 High-dimensional network analytics: mapping topic networks in Twitter data during the Arab Spring
      1. 10.1 Introduction
      2. 10.2 Arab Spring
      3. 10.3 General background
      4. 10.4 Data
      5. 10.5 The social pulse: geo-temporal trends in Twitter topics and users
        1. 10.5.1 Methodology
        2. 10.5.2 Topic overview
        3. 10.5.3 Over time analysis
        4. 10.5.4 Characterization of user–topic similarity network
        5. 10.5.5 Social interaction overview: the reply network
        6. 10.5.6 Characterization of group structure
        7. 10.5.7 Key actors
      6. 10.6 Discussion
      7. 10.7 Conclusion
      8. Acknowledgements
      9. References
    3. 11 Social influence analysis in the big data era: a review
      1. 11.1 Introduction
      2. 11.2 Social influence measurement
        1. 11.2.1 Network-based measures
        2. 11.2.2 Behavior-based measures
        3. 11.2.3 Interaction-based measures
        4. 11.2.4 Topic-based measures
        5. 11.2.5 Other measures
      3. 11.3 Influence propagation and maximization
        1. 11.3.1 Opinion leader identification
        2. 11.3.2 Influence maximization
        3. 11.3.3 Diffusion network inference
        4. 11.3.4 Challenges of IP&M
      4. 11.4 Challenges in big data
      5. 11.5 Summary
      6. Acknowledgement
      7. References
  11. Part IV Big data over biological networks
    1. 12 Inference of gene regulatory networks: validation and uncertainty
      1. 12.1 Introduction
      2. 12.2 Background
        1. 12.2.1 Markov chains
        2. 12.2.2 Logical regulatory networks
        3. 12.2.3 Control policy for maximal steady-state alteration
        4. 12.2.4 Inference algorithms
      3. 12.3 Network distance functions
        1. 12.3.1 Semi-metrics
        2. 12.3.2 Rule-based distance
        3. 12.3.3 Topology-based distance
        4. 12.3.4 Transition-probability-based distance
        5. 12.3.5 Steady-state distance
        6. 12.3.6 Control-based distance
      4. 12.4 Inference performance
        1. 12.4.1 Measuring inference performance using distance functions
        2. 12.4.2 Analytic example
        3. 12.4.3 Synthetic examples
      5. 12.5 Consistency
      6. 12.6 Approximation
      7. 12.7 Validation from experimental data
        1. 12.7.1 Metastatic melanoma network inference
      8. 12.8 Uncertainty quantification
        1. 12.8.1 Mean objective cost of uncertainty
        2. 12.8.2 Intervention in yeast cell cycle network with uncertainty
      9. References
    2. 13 Inference of gene networks associated with the host response to infectious disease
      1. 13.1 Background
      2. 13.2 Factor models in gene expression analysis
      3. 13.3 Factor models
        1. 13.3.1 Shrinkage prior
        2. 13.3.2 Multiplicative gamma process
      4. 13.4 Discriminative models
        1. 13.4.1 Bayesian log-loss
        2. 13.4.2 Bayesian hinge-loss
      5. 13.5 Discriminative factor model
        1. 13.5.1 Multi-task learning
      6. 13.6 Inference
      7. 13.7 Experiments
        1. 13.7.1 Performance measures
        2. 13.7.2 Experimental setup
        3. 13.7.3 Classification results
        4. 13.7.4 Interpretation
      8. 13.8 Closing remarks
      9. 13.9 Inference details
      10. Acknowledgements
      11. References
    3. 14 Gene-set-based inference of biological network topologies from big molecular profiling data
      1. 14.1 Introduction
      2. 14.2 Big data to network components
      3. 14.3 Gene sets related to network components
      4. 14.4 Reconstructing biological network topologies using gene sets
        1. 14.4.1 A general setting
        2. 14.4.2 Gene set Gibbs sampling
        3. 14.4.3 Gene set simulated annealing
      5. 14.5 Discussion and future work
      6. References
    4. 15 Large-scale correlation mining for biomolecular network discovery
      1. 15.1 Introduction
      2. 15.2 Illustrative example
        1. 15.2.1 Pairwise correlation
        2. 15.2.2 From pairwise correlation to networks of correlations
      3. 15.3 Principles of correlation mining for big data
        1. 15.3.1 Correlation mining for correlation flips between two populations
        2. 15.3.2 Large-scale implementation of correlation mining
      4. 15.4 Perspectives and future challenges
        1. 15.4.1 State-of-the-art in correlation mining
        2. 15.4.2 Future challenges in correlation mining biomolecular networks
      5. 15.5 Conclusion
      6. Acknowledgements
      7. References
  12. Index