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Table of contents
1 State of the Art
1.1 Static Centralities
1.1.1 Shortest path based centralities
1.1.2 Spectral Centralities
1.2 Temporal Centralities
1.2.1 Online Algorithms
1.2.2 Shortest path based centrality adaptations
1.2.3 Spectral centrality adaptations
1.2.4 Conclusion
1.3 Approximation
1.3.1 Static graphs
1.3.2 Evolving graphs
2 Datasets
2.1 Email-exchange Networks
2.2 Co-occurrence networks
2.3 Social Networks
2.4 Motion networks
2.5 Conclusion
3 Temporal Centralities under scrutiny
3.1 Centralities
3.1.1 Temporal Closeness
3.1.2 Snapshot
3.1.3 Temporal Eigenvector
3.1.4 Coverage Centrality
3.2 Comparison protocol
3.2.1 Ranking
3.2.2 Kendall Tau correlation
3.2.3 Difference in ranks
3.2.4 Global Importance
3.3 Results
3.3.1 Global Observation
3.3.2 Impact on individual nodes
3.3.3 Identifying globally important nodes
3.4 Discussion
3.4.1 Average centrality
3.4.2 Absence of importance
3.4.3 Ranking methods
3.4.4 Conclusion
4 Approximation and Identification
4.1 Less computation
4.1.1 Conclusion
4.2 Identifying important nodes
4.2.1 Strategies
4.2.2 Best combination
4.2.3 Results
4.2.4 Ordering
4.2.5 Conclusion
4.3 Approximation
4.3.1 Exponential function
4.3.2 Sigmoid function
4.3.3 Conclusion
4.4 Overall protocol
4.4.1 Methodology
4.4.2 In practice
4.5 Discussion
4.5.1 Absence of importance
4.5.2 Conclusion
5 Ego-betweenness
5.1 Ego-centric vision
5.1.1 Ego-graph and ego-dynamic graph
5.1.2 Paths
5.2 Ego-betweenness
5.2.1 Definition
5.2.2 Computation
5.3 Dynamical betweenness under scrutiny
5.3.1 Common base
5.3.2 Datasets
5.3.3 Comparison tools
5.4 Results
5.5 Conclusion
6 Conclusion and perspectives
Appendices
A Approximation and Identification
A.1 Relative Error per multiple of Iop
B P2PTV Multi-channel Peers Analysis
B.1 Introduction
B.2 Related Works
B.3 DataSet
B.4 Global analysis
B.4.1 Tracking exchanges of video content
B.4.2 Presence multi-channel peers
B.5 Exploiting sliding time windows
B.5.1 Different peer behavior
B.5.2 Different super-peers behavior
B.6 Extending the analyses
B.6.2 Comparisons of the datasets
B.7 Conclusion




