Official course description:
Basic info last published 26/02-19

Network Analysis

Course info
Language:
English
ECTS points:
7.5
Course code:
BSNEANA1KU
Offered to guest students:
-
Offered as a single subject:
-
Programme
Level:
Bachelor
Programme:
Bachelor of Science in Data Science
Staff
Course semester
Semester
Efterår 2018
Start
27 August 2018
End
28 December 2018
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract

The course is focused on forming the basis of a network scientist. The final objective is to have the students being able to fully appreciate the difficulties of the problem of finding communities in social networks. To achieve this objective, a complete knowledge of network science is required. Each concept necessary to understand communities in networks has to be fleshed out in previous lectures, and the concepts on which it depends have to be presented beforehand.

Description

Networks science is a thriving field of study. The reason of its popularity is its ability to represent very complex phenomena with the very simple model of a graph. With network science one can analyze global societal patterns, catastrophic breakdowns of distributed infrastructures, the metabolic pathways in humans at the basis of diseases, among many examples.

1.    Basic Graph Theory
2.    Basic Graph Theory #2
3.    Basic Graph Properties
4.    Distances, diameter & centrality metrics
5.    Graphs as Matrices
6.    Graph Models
7.    Real World Networks
8.    Events on Graphs
9.    Introduction to the Mesoscale
10.    Community Discovery
11.    Community Discovery #2
12.    Community Discovery #3

Formal prerequisites
Changes may occur The course is only open to BSc DS third semester.
Intended learning outcomes

After the course, the student should be able to:

  • Define various types of network structure and calculate the main descriptive metrics
  • Describe the main characteristics of a given network structure
  • Analyse real world social networks and summarize their main characteristics and how those affect network-based processes (e.g. propagation)
  • Identify community structures in network and provide an appropriate interpretation of these structures.
Ordinary exam
Exam type:
A: Written exam, external (7-trinsskala)
Exam variation:

Exam description:
Duration: 4 hours Restrictions: Only NetworkX and/or iGraph and an IDE can be used during the exam