Barabasilab-networkscience Study notes-graph theory

The first contact with complexity science is in a book called Think Complexity, Dr. Allen speaks very well of data structure and complexity science, Barabasi is a well-known complex network scientist, Barabasilab is a laboratory he dominates, Here the note is about the course introduced in the slider notes, of course, other people's course is not a public class, so from the PPT can only see the backbone of things, to add, slider related books here can be found

The graph theory is the things that were introduced in the previous graph theory, these things have been in contact with the university, and now it is promoted to the angle of scientific application.

Graph theory has three core and basic concepts:

1. Degree Distribution:p (k)
2. Path Length: <d>
3. Clustering Coefficient:c

The first concept is the degree of node: In reality, the degree of the network is far less than the maximum possible value, in the example of the largest in the list of less than 100 and the node tens of thousands

The second concept is the path length (about edges)

The third concept is clustering coefficient, which reflects the aggregation of nodes around a node

In addition, there are some pairs of nodes in graph theory and matrix theory, that is, graphs can be transformed into matrices, they have certain object relations.

The details of the concepts and theories here can be seen in the PPT and text Book of this course, as well as on the wiki in terms of the keywords mentioned above.

Reference documents:

Http://barabasilab.neu.edu/courses/phys5116/content/Class2_2014_Graphs.pdf

Http://barabasilab.neu.edu/networksciencebook/downlPDF.html

Barabasilab-networkscience Study notes-graph theory