Python programming and Network models
ff2n9b47 -- Tue 10-12, North (Észak) 3.92 -- Feb 9,16,25, Mar 1,3(8-10am),8,22, Apr 12,19,26, May 3,10
libraries: http://graph-tool.skewed.de, http://igraph.org/python
2016-04-19: Random graph and small world model
2016-04-12: Network properties → Starting Student projects
2016-03-22: Mapping a small part of the WWW → Homework & Solutions
2016-03-08: Pattern matching → Homework & Solutions
2016-03-03: Generating Scale-Free (SF) network, computing its degree distribution
2016-03-01: Giant component of ER network → Homework & Solutions
2016-02-23: Generating ER network faster, speed tests
2016-02-16: Generating random (Erdos-Renyi) network → Homework & Solutions
2016-02-09: Mean and std.dev. of N rnd numbers → Homework & Solutions
2016-02-01: Please install Python and a text editor for coding on your laptop. Click here for help.
This course is an updated version of a previous course on Perl programming and networks.
It aims to help students reach the level where they can routinely apply and combine two major tool sets of
current quantitative research: Python and Network models.
Currently, Python is a major programming language
(i) in physics from the microscopic to the largest length scales,
(ii) in computational biology,
(iii) in large-scale social and technological networks, and other fields.
Networks provide a quantitative tool set for analyzing many-particle interacting systems and complex data.
They are intuitive and can be efficiently connected to linear algebraic, stochastic and other methods.
Python is taught through examples from "Learning Python" and "Learn Python the Hard Way", while networks are taught with the "Network Science Book". Homework is largely based on examples taken from the previous version of this course.
with Gergely Palla the course of Tamas Vicsek on the Statistical physics of biological systems.
Perl programming and Networks (several versions).