**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 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:** The 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 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).