Online Social Networks: Perspectives, Applications and Developments

Name: Online Social Networks: Perspectives, Applications and Developments
Brand: Nova Science Publishers Inc
Price: 197.99 EUR
Availability: OnlineOnly

Wei Tan Chee(Editor)

Nova Science Publishers Inc

Published on 31. August 2020

231 pages

E-Book

PDF with Adobe-DRM

System requirements

978-1-5361-7388-8 (ISBN)

€197.99incl. 7% vat

System requirements

for PDF with Adobe-DRM

E-Book Single Licence

Available for download

Description

More details

Other editions

Content

Intro
ONLINE SOCIAL NETWORKSPERSPECTIVES, APPLICATIONSAND DEVELOPMENTS
ONLINE SOCIAL NETWORKSPERSPECTIVES, APPLICATIONSAND DEVELOPMENTS
CONTENTS
PREFACE
Chapter 1GENERALIZED MODULARITY EMBEDDING:A GENERAL FRAMEWORKFOR NETWORK EMBEDDING
Abstract
1. INTRODUCTION
2. THE PROBABILISTIC FRAMEWORK FORGENERALIZED MODULARITY EMBEDDING
2.1. The Generalized ModularityMatrix of a Sampled Graph
2.2. Community Detection
2.3. Modularity Embedding
3. EXTENSIONS AND APPLICATIONS OFGENERALIZED MODULARITY EMBEDDING
3.1. Modularity Embedding for Data Points in a Semi-MetricSpace
3.2. The Laplacian Eigenmaps as a Special Case of GeneralizedModularity Embedding
3.3. Dimensionality Reduction and Connections to PCA
4. NONNEGATIVE EMBEDDING AND CLUSTERING
5. EXPERIMENTS
5.1. Performance on the Six Clusters Dataset
5.2. Performance on the Amazon Dataset
5.3. Performance on the Flickr Dataset
CONCLUSION AND FUTURE WORK
APPENDIX A. PROOF OF THEOREM 3
APPENDIX B. PROOF OF THEOREM 5
REFERENCES
Chapter 2EPIDEMIC SOURCE DETECTIONIN COMPLEX NETWORKS
Abstract
1. INTRODUCTION
2. PROBLEM SETTING
2.1. NetworkModel
2.2. Epidemic Model
2.3. Epidemic Source Localization
3. MATHEMATICAL MODELS OF DIFFUSION
3.1. Exact and Approximate Infection Likelihoods
3.2. Distance-Dependent Infection Likelihood
4. RUMOUR SOURCE DETECTION,WITH KNOWNACTIVATION TIME
4.1. Estimation of Monitor Location
4.1.1. Estimation of Monitor Location with Artificial Rumour Spreading
4.1.2. Estimation of Monitor Location with a K-Medoids Approach
4.2. Estimation of a Set of Candidate Sources
4.3. Single Source Estimation
4.4. Source Detection Algorithm
4.5. Experimental Results
5. RUMOUR SOURCE DETECTION, WITH UNKNOWNACTIVATION TIME
5.1. Estimation of Monitor Locations
5.1.1. Estimation of Monitor Locations using a Fixed Rumour Start Time
5.1.2. Simultaneous Estimation of Monitor Location and RumourStart Time
5.2. Estimation of a Set of Potential Sources
5.3. Experimental Results
CONCLUSION
REFERENCES
Chapter 3SUBGRAPH NETWORK FOR EXPANDINGSTRUCTURAL FEATURE SPACEWITH APPLICATION TO GRAPH DATAMINING
Abstract
1. SUBGRAPH NETWORKS
1.1. Different-Order SGNs
1.2. First-Order SGN
1.3. Second-Order SGN
2. APPLICATIONS OF SGNS
2.1. LinkWeight Prediction
2.1.1. Feature Indices
2.1.2. Experimental Results
2.2. Node Classification
2.2.1. Method
2.2.2. Experimental Results
2.3. Graph Classification
2.3.1. Graph Representation
2.3.2. Experiments
3. APPENDIX
3.1. SGN(0) Indices
3.2. SGN(1) Indices
CONCLUSION
REFERENCES
Chapter 4ONLINE INFORMATION SPREADINGAND SOURCE IDENTIFICATION
Abstract
1. INTRODUCTION
1.1. Information Spreading Models
1.2. Information Source Identification
2. CENTRALITY METHODS
2.1. Single Source
2.2. Multiple Sources
2.3. Different Infection Start Times
3. BAYESIAN INFERENCE
3.1. MLE
3.2. Sequential Source Estimation
4. THE GROMOV METHOD
5. MACHINE LEARNING USING GRAPH FLOWCONVOLUTIONAL NETWORK
CONCLUSION
REFERENCES
Chapter 5THE PAGERANK BIPARTITE GRAPHRANKING: ONLINE CHAT GROUPRECOMMENDATION
Abstract
1. INTRODUCTION
2. PROBLEM FORMULATION
3. PRELIMINARY CONCEPTS
3.1. Graph Ranking
3.1.1. PageRank
3.1.2. Hypertext Induced Topic Selection
3.2. Ranking on Bipartite Graph
4. WEIGHTED PAGERANK ALGORITHM
5. EXPERIMENT
5.1. Experiment Setup
5.2. Experiment Result
CONCLUSION
REFERENCES
Chapter 6PARALLEL COUNTING OF SUBGRAPHS INLARGE GRAPHS: PRUNING ANDHIERARCHICAL CLUSTERING ALGORITHMS
Abstract
1. INTRODUCTION
2. ALGORITHMS AND ANALYSIS
2.1. Pruning Step
2.2. Hierarchical Clustering Step
2.3. Counting Step
2.3.1. Inter-Cluster Triangles with One Vertex as the Root
2.3.2. Non-Root Inter-Cluster Triangles
2.3.3. Intra-Cluster Triangles
2.4. Algorithm Implementation
2.5. Time Complexity
3. PERFORMANCE EVALUATION
3.1. Experimental Setup
3.2. Comparison between Different Algorithms
3.3. Algorithm on Large Graphs in Public Domain
3.4. Trade-Off between Pruning and Hierarchical Clustering
3.5. MapReduce Software Implementation
4. EVALUATION USING LARGE RANDOM GRAPHS
CONCLUSION AND FUTURE WORK
REFERENCES
Chapter 7SOCIAL LEARNING NETWORKAND ITS APPLICATIONS IN LARGE SCALEONLINE EDUCATION THROUGH CHATBOT
1. INTRODUCTION
2. LEARNING TASK SCHEDULING PROBLEM
3. OPTIMAL TASK SCHEDULING FRAMEWORK
3.1. Optimization Problem Formulation
3.2. Optimality Characterization
4. HUMAN-ASSISTED COMPUTATION FORAUTO-GRADING
5. AUTO-GRADING FRAMEWORK FOR MCQS
6. EXTENSION TO IMAGE ANNOTATIONASSESSMENT
7. APPLICATION IN MESSENGER CHATBOT
7.1. Optimal Learning Task Scheduling Frame
7.2. Automated Grading Framework
8. FUTURE WORK
CONCLUSION
REFERENCES
Chapter 8RUMOR SOURCE DETECTIONIN FINITE GRAPHS WITH BOUNDARYEFFECTS BY MESSAGE-PASSINGALGORITHMS
Abstract
1. INTRODUCTION
1.1. Our Contributions
2. PRELIMINARIES OF RUMOR CENTRALITY
3. TREES WITH A SINGLE END VERTEX
3.1. Impact of Boundary Effects on P(Gn|v)
3.2. Analytical Characterization of Likelihood Function
3.3. Optimality Characterization of Likelihood Estimate
3.4. Likelihood Ratio Between Centroid and End Vertexon Different Network Topology
4. TREES WITH MULTIPLE END VERTICES
4.1. Degree-Regular Tree (d 3) Special Case:Gn Is Broom-Shaped
4.2. Message-Passing Algorithm
4.3. Simulation Results for Finite d-Regular Tree Networks
4.4. Simulation Results for Finite General Tree Networks
CONCLUSION
REFERENCES
ABOUT THE EDITOR
INDEX
Blank Page

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Online Social Networks: Perspectives, Applications and Developments

Description

More details

Other editions

Additional editions

Content

System requirements