A roadmap to navigate Lecture 1 of Graph Theory course —Fall 2019.

This course comes at the intersection of mathematics, learning, and algorithms.

PDF of the lecture note is available at: [ Ссылка ]

*** 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),
[ Ссылка ]

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This lecture will give you a flavor of different aspects of the broad and ever-expanding field of graph theory and analysis.
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1. Introduction to graph theory
1.1 Motivation and broad introduction
1.2 Graph representation (nodes, edges, types of graphs, directionality, sparsity, adjacency matrix)
1.3 Types of graphs including simple, complete, hypergraphs, and decorated or annotated graphs
1.4. Introduction to graph topology (degree of a node)



**** Resources and further readings ****

1. [ Ссылка ]
2. [ Ссылка ]
3. [ Ссылка ]
4. Decorated/annotated graphs: Murphy, A.C. et al. Explicitly linking regional activation and function connectivity:
community structure of weighted networks with continuous annotation. Preprint
at [ Ссылка ] (2016).
5. [ Ссылка ]
6. [ Ссылка ] (hypergraphs)

 Art gallery problem: [ Ссылка ]

**** Python code (random graph generator) ****
[ Ссылка ]


*** More for eager learners ***
1) Guide to effective learning: [ Ссылка ]

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