+ indep. WoS citations

Python programming and Network models, ff2n9b47

Spring 2017 — Scores

ELTE North 3.92, Tue 12.15-13.45, Feb/14,21,28 - Mar/7,14,21,28 - Apr/4,25 - May/2,9,16
2017-05-16: Examples: scatter plot with binning, a small web spider, clustering with k-clique percolation.
2017-05-09: Agglomerative hierarchical clustering based on a weighted network.
2017-05-02: Attack vs error tolerance. PDF of paper. See also Chapter 8.4 of the Network Science Book.
2017-04-25: Small-World model. PDF of Watts-Strogatz paper.
2017-04-04: Pattern matching in Python. → Starting Student projects
2017-03-28: Colleagues tell in 15 mins each how they use Python.
2017-03-21: Scale-Free model def. Derivation: \(k(t)\sim t^{1/2}\), \(p(k)\sim k^{-3}\). → Homework & Solution
2017-03-14: Clustering coeff. Graph component. Distance. ER model: definitions. → Homework & Solution
2017-03-07: Discussing homework. Node degree, avg., distribution. Using functions. → Homework & Solution
2017-02-28: Set: add, remove, in. Function with string, list, dict, set. Hist, pdf, cdf. → Homework & Solution
2017-02-21: List: append, sort, indexing, slices, pop, in. Dict: keys, values. → Homework & Solution | +
2017-02-14: Hello World. Arguments. Reading a file. Printing. Numbers, operations. → Homework & Solution
2017-02-07: Preparatory meeting. Please install Python and a text editor for coding on your laptop. I can help.

Spring 2016

ELTE North 3.92, Tue 10.15-11.45, Feb/9,16,25, Mar/1,3(8.15-9.45),8,22 Apr/12,19,26 May/3,10
2016-05-10: Examples: Is there a characteristic length in human text and DNA? How does property value change?
2016-05-03: Protein-protein association networks. Clustering with k-clique percolation.
2016-04-26: Scale-Free model. Girvan-Newman clustering.
2016-04-19: Random Graph model. Small World model.
2016-04-12: Network properties → Starting Student projects
2016-03-22: Mapping a small part of the WWWHomework & Solutions
2016-03-08: Pattern matchingHomework & Solutions
2016-03-03: Generating Scale-Free (SF) network, computing its degree distribution
2016-03-01: Giant component of ER networkHomework & Solutions
2016-02-23: Generating ER network faster, speed tests
2016-02-16: Generating random (Erdos-Renyi) networkHomework & Solutions
2016-02-09: The mean and std.dev. of \(N\) rnd numbersHomework & 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 programming (coding) 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 several 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". Some of the assignments are similar to the problems in the previous version of this course, and many of them are new questions.

Fall 2015: with Gergely Palla the course of Tamas Vicsek on the Statistical physics of biological systems.

Before 2015: Perl programming and Networks (several versions).