News People Research & Projects Publications Teaching Calendar Video Resources Archive Open Theses Cooperations Contact Us Intranet

Computational and Visual Network Analysis (4DV809)

Instructor

Dr. Kostiantyn Kucher

Guest Lecturers

Prof. Dr. Andreas Kerren
Dr. Ilir Jusufi

Course Examiner

Prof. Dr. Andreas Kerren

Time & Place

Compare here! The lectures, seminars, and tutoring sessions will take place online via Zoom. Additional video materials will be shared via the Moodle course room.

Teaching Period

IV (2021-03-22 till 2021-06-06)

Assessment

Assignments, oral presentations, and oral examinations. Oral examinations will take place via Zoom in week 22.

Prerequisites

90 credits in Computer Science (including a degree project at Bachelor level), and English B/English 6 (or the equivalent).

Credits

5 ECTS

Topic

Introduction to Analytical Methods for Network Data

This course provides an introduction to the variety of analytical methods for relational data, i.e., graphs and networks. Such data types are applied for numerous tasks within computer science, software engineering, and other domains such as social sciences, bioinformatics, security, etc.

This course addresses three perspectives: 1) computational network analysis, e.g., automatic identification of the most influential nodes; 2) graph drawing for automatic layout of nodes and edges; and 3) information visualization for interactive representation and exploration of networks and associated data. After finishing the course, the students should be able to choose and develop the most suitable visual network analytic technique for the given task, cf. the intended learning outcomes in the course syllabus.

The courses Information Visualization and Advanced Information Visualization and Applications complement this specialized course with a more in-depth view into the theory and state-of-the-art of interactive information visualization and its applications in various domains and for various data types (beyond graph/network data only). The full-term course Project In Visualization and Data Analysis provides a more applied practical perspective on visual analytics and focuses on project team work on a visual analytic tool implementation.

Schedule

Preliminary Schedule (Lectures):

#	Date	Topic	Slides	Videos
01	2021 03 24	Course Introduction
02	2021 03 31	Foundations of Graph and Network Analyses I
03	2021 04 07	Foundations of Graph and Network Analyses II
04	2021 04 14	Network Analysis Tasks and Models
05	2021 04 21	Graph Drawing Approaches
06	2021 04 28	Foundations of Network Visualization
07	2021 05 05	Multivariate Networks
08	2021 05 12	Temporal Networks
09	2021 05 19	Multilayer Networks
10	2021 05 26	Further Topics and Applications of Network Analysis

Preliminary Schedule (Seminars):

#	Date	Topic
1	2021 03 24	Course Assignments Introduction
2	2021 03 31	Assignment 1 Discussion
3	2021 04 21	Assignment 2 Presentation
4	2021 05 12	Work-in-Progress Discussion
5	2021 05 26	Assignment 3 Presentation

Materials

Learning Environment:

Moodle

Visualization Tools and Libraries:

D3 (JavaScript)
Vega (JavaScript; based on D3)
Vega-Lite (JavaScript; based on D3 and Vega)
plotly.js (JavaScript; based on D3)
nivo (JavaScript; based on D3 and React)
Victory (JavaScript; based on D3 and React)
Chart.js (JavaScript)
vis.js (JavaScript)
Highcharts (JavaScript)
Google Charts (JavaScript)
Rickshaw (Temporal data; JavaScript; based on D3)
Leaflet (Geospatial data (maps); JavaScript)
Sigma (Graph/network data; JavaScript)
Bokeh (Python; generates a web-based visualization)
Dash (Python or R; generates a web-based visualization using plotly.js)
plotly.py (Python; generates a web-based visualization using plotly.js)
Altair (Python; generates a web-based visualization using Vega and Vega-Lite)
mpld3 (Python; generates a web-based visualization using D3)
Shiny (R; generates a web-based visualization)
Shiny Dashboard (R; generates a web-based visualization using Shiny)
Tableau (Visualization environment, Dashboards)
QlikView (Visualization environment, Dashboards)
Power BI (Visualization environment, Dashboards)
Visualize Free (Online, Dashboards)
Keshif Online (Online, Dashboards)
yFiles (Several platforms)
Gephi (The Open Graph Viz Platform; Java)
Processing (Environment for graphics programming; several platforms)
Improvise (Java)
JFreeChart (Java)
ggplot2 (R)
Chaco (Python)
Matplotlib (Python)

No longer in active development (not recommended for assignments):

JavaScript InfoVis Toolkit (JavaScript)
Flare - Data Visualization for the Web (ActionScript)
JUNG Java Universal Network/Graph Framework (Java)
InfoVis Toolkit (Java)
JChart (Java)
XmdvTool (Qt)

Interesting URLs:

Assignments

The practical part of the course assessment consists of three assignments forming a single programming project, and the corresponding seminar presentations. Assignment instructions and submission pages will be provided in the Moodle course room.