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Graph Theory Blink 2.4 (Eigenvector centrality and PageRank centrality)
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A roadmap to navigate Graph Theory Blinks.
This course comes at the intersection of mathematics, learning, and algorithms.
*** Primarily textbooks:
1) Bullmore, Edward T._ Fornito, Alex_ Zalesky, Andrew - Fundamentals of Brain Network Analysis-Academic Press,Elsevier (2016)
2) Arthur Benjamin, Gary Chartrand, Ping Zhang - The Fascinating World of Graph Theory-Princeton University Press (2015)
3) (Graduate Texts in Mathematics) Reinhard Diestel - Graph theory-Springer (2006)
*** Library: SNAP library (network analysis tool),
=========================================================================================
Lecture 2 will cover centrality measures in graphs for discovering influential nodes
=========================================================================================
2. Graph operations and topology: tools and measures (week 2)
2.1 Degree distributions
2.2 Centrality measures [chapter 5 from Fundamentals of Brain Network Analysis]
2.2.1 Degree centrality
2.2.2 Eigenvector centrality
2.2.3 Closeness centrality
2.2.4 Betweenness centrality
2.2.5 Delta centrality
**** Resources and further readings ****
**** Source code ****
*** More for eager learners ***
This course comes at the intersection of mathematics, learning, and algorithms.
*** Primarily textbooks:
1) Bullmore, Edward T._ Fornito, Alex_ Zalesky, Andrew - Fundamentals of Brain Network Analysis-Academic Press,Elsevier (2016)
2) Arthur Benjamin, Gary Chartrand, Ping Zhang - The Fascinating World of Graph Theory-Princeton University Press (2015)
3) (Graduate Texts in Mathematics) Reinhard Diestel - Graph theory-Springer (2006)
*** Library: SNAP library (network analysis tool),
=========================================================================================
Lecture 2 will cover centrality measures in graphs for discovering influential nodes
=========================================================================================
2. Graph operations and topology: tools and measures (week 2)
2.1 Degree distributions
2.2 Centrality measures [chapter 5 from Fundamentals of Brain Network Analysis]
2.2.1 Degree centrality
2.2.2 Eigenvector centrality
2.2.3 Closeness centrality
2.2.4 Betweenness centrality
2.2.5 Delta centrality
**** Resources and further readings ****
**** Source code ****
*** More for eager learners ***
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