Note: Intro -- Social Media Modeling and Computing -- Preface -- Contents -- Contributors -- Part I: Social Media Content Analysis -- Quantifying Visual-Representativeness of Social Image Tags Using Image Tag Clarity -- 1 Introduction -- 2 Related Work -- 2.1 Clarity Score -- 2.2 Tagging Images -- 3 Image Tag Clarity -- 3.1 Image Tag Clarity Score -- 3.2 Normalized Image Tag Clarity Score -- 3.3 Time Complexity -- 4 Performance Evaluation -- 5 Observations Related to Image Tag Clarity Scores -- 5.1 Image Tag Clarity Score Distribution -- 5.2 Tag Usage Pattern -- 5.2.1 Tag Visual-Representativeness vs. Tag Frequency -- 5.2.2 Visual Tags vs. Non-visual Tags -- 5.3 Tag Co-occurrence Pattern -- 6 Conclusions and Future Work -- References -- Tag-Based Social Image Search: Toward Relevant and Diverse Results -- 1 Introduction -- 2 Related Work Review -- 2.1 Social Image Search -- 2.2 Diversifying Image Search Result -- 2.3 Performance Evaluation Metric -- 3 Diverse Relevance Ranking -- 3.1 Average Precision -- 3.2 Average Diverse Precision -- 3.3 Diverse Relevance Ranking -- 4 Relevance and Similarity of Social Images -- 4.1 Relevance Estimation -- 4.2 Similarity Estimation -- 5 Empirical Study -- 5.1 Flickr Dataset -- 5.2 Empirical Results -- 5.3 Complexity Analysis -- 6 Conclusion -- References -- Social Image Tag Ranking by Two-View Learning -- 1 Introduction -- 2 Related Work -- 3 Two-View Learning for Social Tag Ranking -- 3.1 Preliminaries -- 3.2 Two-View Representation for Social Tags -- 3.3 Two-View Learning for Tag Weighting -- 3.4 Algorithm -- 3.5 Similarity Measure for Building Graphs -- 3.5.1 Similarity in Visual Space -- 3.5.2 Similarity in Concept Space -- 3.6 Speedup by Clustering -- 4 Experiments -- 4.1 Experimental Testbed and Setup -- 4.2 Performance Evaluation on Tag-based Social Image Retrieval. , 4.2.1 Ranking Schemes and Evaluation Metric -- 4.2.2 Results of Image Retrieval Accuracy -- 4.3 Evaluation of Computation Efficiency -- 4.4 Evaluation of Hyper-parameters -- 4.5 Application to Auto Tag Recommendation -- 5 Limitations and Discussions -- 6 Conclusions -- References -- Combining Multi-modal Features for Social Media Analysis -- 1 Introduction -- 1.1 Social Media -- 1.2 Multi-modal Analysis -- 1.3 Examined Approaches & -- Applications -- 2 Related Work -- 2.1 Methods for Multi-modal Analysis -- 2.2 Multi-modal Analysis of Social Media -- 2.3 Social Media Applications with Multi-modal Analysis -- 3 Combining Heterogeneous Information Using Ant Colony Optimization -- 3.1 Problem Formulation -- 3.2 The Proposed Framework -- 3.3 Experimental Study -- 4 Combining Heterogeneous Information Using Aspect Models -- 4.1 Motivation & -- Approach -- 4.2 Representing Images Based on the Co-occurrence of Words -- 4.2.1 Visual-Word Co-occurrence Image Representation -- 4.2.2 Tag-Word Co-occurrence Image Representation -- 4.3 Representing Images Using Aspect Models -- 4.4 Experimental Study -- 4.4.1 Test-bed -- 4.4.2 Evaluation Metric -- 4.4.3 Results -- 5 Conclusions -- References -- Multi-label Image Annotation by Structural Grouping Sparsity -- 1 Introduction -- 2 Sparse Model for Feature Selection -- 2.1 Notation -- 2.2 Lasso -- 2.3 Elastic Net -- 2.4 Group Lasso -- 3 Multi-label Boosting by Structural Grouping Sparsity -- 3.1 Problem Formulation and Solution -- 3.1.1 Step 1: Regression with Structural Grouping Penalty -- 3.1.2 Step 2: Multi-label Boosting by Curds and Whey -- 3.2 Regularized Regression with Structural Grouping Penalty -- 3.2.1 Group Selection -- 3.2.2 Subgroup Identification by GSCD -- 3.3 Multi-label Boosting by Curds and Whey -- 4 Experiments -- 4.1 Experimental Configuration -- 4.1.1 Dataset -- 4.1.2 Evaluation Metrics. , 4.2 Parameter Tuning -- 4.3 Heterogeneous Feature Selection -- 4.4 Performance of Multi-label Learning and Annotation -- 5 Conclusion -- References -- Part II: Social Media System Design and Analysis -- Mechanism Design for Incentivizing Social Media Contributions -- 1 Introduction -- 2 Related Work and Domains -- 2.1 Why Are Social Media Networks Different? -- 2.2 Related Work in Social Media -- 3 Background: Game Theory -- 4 A Game-Theoretic Framework: The User Viewpoint -- 4.1 Problem Motivation -- 4.2 Problem Formulation -- 4.3 Approach -- 4.3.1 Two Agents, One Social Media Task -- 4.3.2 N Agents, One Social Media Task -- 4.4 First Insights -- 4.5 Individual vs. Group Rationality -- 5 The Designer's Viewpoint -- 5.1 Problem Formulation -- 5.2 Mechanism 1: Paying the Extra Benefit -- 5.3 Mechanism 2: Social Incentives -- 5.4 Mechanism 3: Second Price Auction Based Mechanism -- 5.4.1 Shapley Value -- 5.5 Case Study -- 5.5.1 Mechanism 1: Paying the Extra Benefit -- 5.5.2 Mechanism 2: Social Incentives -- 5.5.3 Mechanism 3: Second Price Auction Based Mechanism -- 5.6 Comparison of Different Approaches -- 6 Discussion and Future Work -- 7 Conclusions -- References -- Efficient Access Control in Multimedia Social Networks -- 1 Introduction -- 2 Related Works -- 2.1 Access Control in Social Networks -- 2.2 Efficient Access Control -- 3 Model of Access Control -- 4 Proposed Efficient Access Control Mechanism -- 4.1 Access Rights Organization -- 4.2 Access Control Mechanism -- 4.3 Modification of Access Rights -- 4.4 Deletion of a Content -- 4.5 Efficient Data Representation -- 5 Grouping of Contents with Same Access Rights -- 5.1 Grouping of Contents -- 5.2 Access Control After Grouping of Contents -- 6 Performance Analysis -- 6.1 Mathematical Performance Analysis -- 6.2 Experimental Performance Analysis -- 7 Conclusion -- References. , Call Me Guru: User Categories and Large-Scale Behavior in YouTube -- 1 Introduction -- 2 Research in YouTube -- 3 YouTube Channels and User Categories -- 4 Extracting User Behavioral Features from YouTube -- 5 Behavioral Data Analysis -- 5.1 How Do Features Correlate? -- 5.2 Are You Special? -- 5.3 How Participative Are You? -- 5.3.1 Directors and Gurus Consistently Upload More Videos -- 5.3.2 Comedians, Musicians, and Reporters Significantly Watch Less Videos -- 5.3.3 Directors, Comedians, and Gurus Favorite the Most -- 5.4 How Social Are You? -- 5.4.1 Incoming Social Features -- 5.4.2 Outgoing Social Features -- 5.5 Male or Female? -- 6 Classifying YouTube Users -- 7 Conclusions -- References -- Social Media Visual Analytics for Events -- 1 Introduction -- 2 Related Work -- 3 Computational Enablers -- 3.1 Relevance -- 3.2 Novelty -- 3.3 Sentiment -- 3.4 Keyword Extraction -- 4 Visual Representations and Interactions -- 4.1 Content Component -- 4.2 Filtering Module -- 4.3 Topic Timeline -- 4.4 Message Volume Graph -- 4.5 Sentiment Timeline -- 4.6 Keywords Component -- 5 Exploratory Study -- 5.1 Procedure -- 5.2 Participants -- 5.3 Results -- 6 Perceived Utility -- 7 Story Angles -- 8 Usage of Interface Features -- 9 Discussion -- 10 Conclusion -- References -- Part III: Social Media Applications -- Using Rich Social Media Information for Music Recommendation via Hypergraph Model -- 1 Introduction -- 1.1 Motivation -- 1.2 Contributions -- 2 Related Work -- 2.1 Hybrid Music Recommendation -- 2.2 Recommendation Using Social Media Information -- 2.3 Graph-Based Ranking and Hypergraph -- 3 Background of Ranking on Graph Data -- 3.1 Regularization Framework for Ranking on Graph -- 3.2 Random Walks with Restarts Model -- 4 Ranking on Unified Hypergraph -- 4.1 Notation and Problem Definition -- 4.2 Regularization Framework for Ranking on Unified Hypergraph. , 5 Music Recommendation via Hypergraph -- 5.1 Data Collection -- 5.2 Acoustic-Based Music Similarity -- 5.3 Unified Hypergraph Construction -- 5.4 Methodology -- 5.5 Computational Complexity Analysis -- 6 Experiments -- 6.1 Compared Algorithms -- 6.2 Evaluation -- 6.3 Performance Comparison -- 6.4 Exploring Parameter Settings -- 6.5 Social Information Contribution -- 7 Conclusions and Future Work -- References -- Using Geotags to Derive Rich Tag-Clouds for Image Annotation -- 1 Introduction -- 2 Survey of Geotagging Related Work -- 3 Tag-Clouds -- 3.1 Cloud of Geotags from Points-of-Interest Databases -- 3.2 Cloud of Community Tags from Flickr Pictures -- 3.3 Cloud of Tags from Personal Photos -- 4 Tag-Cloud Visualization Tool -- 5 Results and User Study -- 6 Conclusion -- References -- Social Aspects of Photobooks: Improving Photobook Authoring from Large-Scale Multimedia Analysis -- 1 Introduction -- 2 Related Work -- 3 Digital Photo Books -- 3.1 Photobook Structure -- 3.2 Test Data Set -- 4 Concept for Social Photobook Analysis -- 4.1 Goals -- 4.1.1 How Social Are Photobooks? -- 4.1.2 How Expressive Are Photobooks? -- 4.1.3 How Vivid Are Photobooks? -- 4.2 Album Classification -- 4.2.1 Assumptions on the Data Set -- 4.2.2 Ground-Truth Determination -- 4.2.3 Analysis -- 4.2.4 Discussion and Classifier Training -- 5 How Social Are Photo Books? -- 5.1 Number of Persons -- 5.2 Person Dominance -- 5.3 Intimacy -- 5.4 Photo Origin -- 6 How Expressive Are Photobooks? -- 6.1 Image-Text Ratio -- Text Items per Page -- Images per Page -- 6.2 Text Lengths and Font Sizes -- 7 How Vivid Are Digital Photobooks? -- 7.1 Color Distribution -- 7.2 Intensity -- 8 Conclusion -- References -- Index.

Note: Intro -- Title Page -- Copyright Page -- Preface -- Acknowledgments -- Table of Contents -- Contributors -- 12 Protein Structure Prediction by Protein Threading -- 12.1 Introduction -- 12.2 Protein Domains, Structural Folds, and Structure Space -- 12.3 Fitting a Protein Sequence onto a Protein Structure -- 12.4 Calculating Optimal Sequence-Structure Alignments -- 12.4.1 PROSPECT -- 12.4.2 RAPTOR -- 12.4.3 Tree-Decomposition-Based Threading Algorithm -- 12.4.3.1 Graph Representation -- 12.4.3.2 Tree Decomposition of Structure Graph -- 12.4.3.3 Tree-Decomposition-Based Alignment Algorithm -- 12.4.3.4 Time Complexity Analysis -- 12.5 Assessing Statistical Significance of Threading Alignments -- 12.6 Structure Prediction Using Protein Threading -- 12.6.1 Database of Template Structures -- 12.6.2 Threading Energy Function -- 12.6.3 Threading Algorithm -- 12.6.4 Assessing Prediction Reliability -- 12.7 Improving Threading-Based Structure Prediction -- 12.7.1 Application of Experimental Data as Threading Constraints -- 12.7.2 Improving Structural Quality through Molecular Dynamics and Energy Minimization -- 12.8 Challenging Issues -- Energy Function -- Threading Algorithm and Implementation -- Statistical Significance Analysis of Threading Results -- Consensus Building and Subdomain Threading -- 12.9 Summary -- Suggested Further Reading -- Acknowledgments -- References -- 13 De Novo Protein Structure Prediction -- 13.1 Introduction -- 13.2 Methods and Algorithms -- 13.2.1 Energy Functions -- 13.2.2 Knowledge-Based Energies -- 13.2.3 Simplified Representations -- 13.2.4 Lattice Methods -- 13.2.5 Fragment Assembly -- 13.2.6 Continuous Torsional Distributions -- 13.2.7 Selection of the Best Conformers -- 13.2.8 PROTINFO, an Example de Novo Prediction Protocol -- 13.2.9 Other de Novo Structure Prediction Protocols -- 13.3 Discussion. , 13.3.1 Faster Computers and Larger Databases -- 13.3.2 Future Directions -- Acknowledgments -- References -- 14 Structure Prediction of Membrane Proteins -- 14.1 Introduction -- 14.2 Secondary Structure Prediction Methods for Membrane Proteins -- 14.2.1 Basic Characteristics -- 14.2.1.1 Hydrophobicity -- 14.2.1.2 Hydrophobic Moment -- 14.2.1.3 The Positive-Inside Rule -- 14.2.2 The Prediction Methods for the Topology of Transmembrane Helices -- 14.2.2.1 Physicochemical Methods Based on Various Hydrophobicity Scales -- Hydropathy Analysis -- An Example -- ALOM2 -- DAS -- PRED-TMR -- SOSUI -- TMFinder -- TopPred -- 14.2.2.2 Statistical (Propensity Based) Methods -- MEMSAT -- TMAP -- TMPRED -- SPLIT -- An Example -- 14.2.2.3 Learning Algorithm-Based Methods -- TMHMM -- HMMTOP -- PHDhtm -- ENSEMBLE -- SVMtm -- 14.2.2.4 Accessibility -- 14.2.3 The Prediction Methods for the Topology of Transmembrane Barrels -- 14.2.3.1 Methods -- B2TMPRED -- BBF -- BETA-TM -- BIOSINO-HMM -- BOMP -- HMM-B2TMR -- OM_Topo_predict -- PRED-TMBB -- ProfTMB -- TBBPred -- TMBETA-NET -- 14.2.3.2 Accessibility -- 14.2.4 Accuracy Measures of Secondary Structure Prediction Algorithms -- 14.2.4.1 Accuracy Measures -- Per-Residue Accuracy -- Per-Segment Accuracy -- 14.2.4.2 Performance of Secondary Structure Predictors -- 14.3 Tertiary Structure Prediction Methods for Membrane Proteins -- 14.3.1 Molecular Determinants of Helix-Helix Interactions -- 14.3.2 Potential (Scoring) Functions of Helix-Helix Interactions -- 14.3.2.1 Potential Functions Based on Physical Models -- 14.3.2.2 "Statistical Potentials -- 14.3.2.3 "Optimal Potential -- 14.3.3 Algorithms for Optimizing Helix-Helix Packing -- References -- 15 Structure Prediction of Protein Complexes -- 15.1 Introduction -- 15.1.1 Protein Docking: Definition -- 15.1.2 Protein-Protein Interactions: Underlying Principles. , 15.1.3 Will the Proteins Interact? -- 15.1.4 Input Structures -- 15.1.5 History of Docking -- 15.2 Unbound Docking: Current Approaches -- 15.2.1 Rigid Body Docking: Search -- 15.2.1.1 Fast Fourier Transform -- 15.2.1.2 Other Search Techniques -- 15.2.2 Rigid Body Docking: Scoring -- 15.2.2.1 Shape Complementarity -- 15.2.2.2 Electrostatics -- 15.2.2.3 Desolvation and Statistical Potentials -- 15.2.2.4 Hydrogen Bonding -- 15.2.3 Refinement -- 15.2.4 Clustering -- 15.2.5 Side Chain Searching -- 15.2.6 Backbone Searching -- 15.3 Evaluation of Docking Algorithms -- 15.3.1 Determining Accuracy of Predictions -- 15.3.2 Docking Benchmark -- 15.3.3 CAPRI Experiment -- 15.4 Case Study: ZDOCK and RDOCK -- 15.4.1 ZDOCK Algorithm -- 15.4.1.1 Angular Search -- 15.4.1.2 ZDOCK Scoring -- 15.4.2 RDOCK -- 15.4.2.1 RDOCK: Energy Minimization -- 15.4.2.2 RDOCK: Scoring -- 15.4.3 6D Refinement -- 15.4.3.1 Development of a Scoring Function -- 15.4.3.2 Exploring the Search Space -- 15.4.3.3 Results -- 15.5 Summary/Future Directions -- 15.5.1 CAPRI Success/Lessons -- 15.5.2 New Developments -- Recommended Reading -- Books -- Review Articles -- References -- 16 Structure-Based Drug Design -- 16.1 Introduction to Modern Drug Discovery -- 16.1.1 Current Drug Discovery Process -- 16.1.2 Role of Protein Structure in Modern Pharmaceutical Sciences -- 16.1.3 Structure-Based Drug Design -- 16.2 Protein Therapeutics -- 16.2.1 Cytokines -- 16.2.2 Antibodies -- 16.2.3 Engineered Enzymes -- 16.2.4 Summary of Protein Therapeutics -- 16.3 Receptor-Based Drug Design -- 16.3.1 Docking -- 16.3.1.1 Search Algorithms -- 16.3.1.2 Scoring Functions -- 16.3.1.3 Input Receptor Structures -- 16.3.1.4 Validation of Docking Algorithms -- 16.3.2 Lead Discovery -- 16.3.2.1 VS Library Generation -- 16.3.2.2 Validation of Docking as a VS Tool -- 16.3.3 Lead Optimization. , 16.3.4 Comparison Studies of Docking Tools -- 16.3.5 Summary of Receptor-Based Drug Design -- 16.4 Ligand-Based Drug Design -- 16.4.1 Pharmacophore Modeling -- 16.4.2 Quantitative Structure-Activity Relationship (QSAR) -- 16.4.2.1 Training Set Compilation -- 16.4.2.2 Descriptor Selection -- 16.4.2.3 Model Generation -- A. Linear Models -- B. Nonlinear Models -- 16.4.2.4 Model Validation -- 16.4.2.5 3D-QSAR -- 16.4.2.6 QSAR Summary -- 16.4.3 Summary of Ligand-Based Drug Design -- 16.5 Future Reading -- 16.6 Conclusions -- References -- 17 Protein Structure Prediction as a Systems Problem -- 17.1 Introduction: The Complexity of Protein Structure Prediction -- 17.2 Consensus-Based Approach for Protein Structure Prediction -- 17.3 Pipeline Approach for Protein Structure Prediction -- 17.4 Expert System for Protein Structure Prediction -- 17.5 From Structure to Function -- 17.6 Benchmark and Evaluation -- 17.7 Genome-Scale Protein Structure Prediction -- 17.7.1 Overview of Three Cyanobacterial Genomes -- 17.7.2 Global Analysis of Protein Structural Folds in Three Genomes -- 17.7.3 Computational Analysis of Predicted Carboxysome Proteins -- 17.8 Summary -- 1. Protein representation -- 2. High-resolution protein structure prediction -- 3. Membrane protein structure prediction -- 4. Effects of protein interaction -- 5. New computational technology development -- Suggested Further Reading -- Acknowledgments -- References -- 18 Resources and Infrastructure for Structural Bioinformatics -- 18.1 Introduction -- 18.2 PDB and Related Databases/Servers -- 18.3 Structure Visualization -- 18.4 Protein Sequence and Function Databases -- 18.5 Structural Bioinformatics Tools -- 18.6 RNA Structure Modeling and Prediction -- 18.7 General Online Resources -- 18.8 Major Journals and Further Readings -- 18.9 Professional Societies, Conferences, and Events -- 18.10 Summary. , Acknowledgments -- References -- Appendix 1 Biological and Chemical Basics Related to Protein Structures -- A 1.1 Amino Acid Residues -- A 1.2 Nucleic Acids -- A 1.3 Protein Structures -- Suggested Further Readings -- Appendix 2 Computer Science for Structural Informatics -- A 2.1 Introduction -- A 2.2 Efficient Data Structures -- A 2.2.1 Hash Tables -- A 2.2.2 Suffix Trees -- A 2.2.3 Disjoint Sets -- A 2.2.4 Heaps -- A 2.2.5 Other Data Structures -- A 2.3 Computational Complexity and NP-Hardness -- A 2.3.1 Concept of Computational Complexity -- A 2.3.2 Optimization Problems -- A 2.4 Algorithmic Techniques -- A 2.4.1 Exhaustive Enumeration -- A 2.4.2 Dynamic Programming -- A 2.4.3 Integer Programming -- A 2.4.4 Branch-and-Bound -- A 2.4.5 A* Search -- A 2.4.6 Dead-End-Elimination Algorithm -- A 2.4.7 Greedy Algorithms -- A 2.4.8 Reduction Techniques -- A 2.4.9 Divide-and-Conquer Algorithms -- A 2.5 Parallel Computing -- A 2.6 Programming -- A 2.7 Summary -- Further Reading -- A 2.8 Acknowledgments -- References -- Appendix 3 Physical and Chemical Basis for Structural Bioinformatics -- A 3.1 Introduction -- A 3.2 Physics Concepts -- A 3.2.1 Units -- A 3.2.2 Potential Energy Surface -- A 3.2.3 Coordinate Systems -- A 3.3 Basic Chemistry -- A 3.3.1 Chemical Reactions -- A 3.3.2 Formation of the Peptide Bond -- A 3.4 Physical Forces in Proteins and Nucleic Acids -- A 3.4.1 Covalent Bond -- A 3.4.2 Electrostatic Interactions -- A 3.4.3 van der Waals Interactions -- A 3.4.4 Hydrogen Bond -- A 3.4.5 Disulfide Bond -- A 3.4.6 Solvation -- A 3.4.7 Hydrophobic Interactions -- A 3.5 Concepts from Statistical Physics and Thermodynamics -- A 3.5.1 Temperature -- A 3.5.2 The Most Probable Distribution -- A 3.5.3 Entropy -- A 3.5.4 Information Entropy -- A 3.5.5 Enthalpy -- A 3.5.6 Free Energy -- A 3.5.6.1 Helmholtz Free Energy -- A 3.5.6.2 Gibbs Free Energy. , A 3.5.7 Kinetic Barrier.