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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 in a hybrid setting (typically room D2272V and/or online via Zoom). Additional video materials will be shared via the Moodle course room.

Teaching Period

IV (2022-03-28 till 2022-06-05)

Assessment

Assignments, oral presentations, and oral examinations. Oral examinations will take place on site or 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	2022 03 28	Course Introduction
02	2022 03 30	Foundations of Graph and Network Analyses I
03	2022 04 04	Foundations of Graph and Network Analyses II
04	2022 04 06	Network Analysis Tasks and Models
05	2022 04 11	Graph Drawing Approaches
06	2022 04 13	Foundations of Network Visualization I
07	2022 04 20	Foundations of Network Visualization II
08	2022 04 25	Multivariate Networks
09	2022 04 27	Temporal Networks
10	2022 05 02	Multilayer Networks
11	2022 05 04	Further Topics and Applications of Network Analysis

Preliminary Schedule (Seminars):

#	Date	Topic
1	2022 03 30	Course Assignments Introduction
2	2022 04 13	Assignment 1 Discussion
3	2022 05 04	Assignment 2 Presentation
4	2022 05 18	Work-in-Progress Discussion
5	2022 06 01	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)
Cytoscape.js (Graph/network data; 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)
Gephi (Visualization environment, graph/network data)
Graphia (Visualization environment, graph/network data)
yFiles (Several platforms)
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.