Visual Analytics for Understanding Relationships between Entities

Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay and Stefan Wrobel
Additional contact information
Natalia Andrienko: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Gennady Andrienko: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Georg Fuchs: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Aidan Slingsby: City, University of London, Northampton Square, Department of Computer Science
Cagatay Turkay: University of Warwick, Centre for Interdisciplinary Methodologies
Stefan Wrobel: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven

Chapter Chapter 7 in Visual Analytics for Data Scientists, 2020, pp 201-228 from Springer

Abstract: Abstract A graph is a mathematical model for representing a system of pairwise relationships between entities. The term “graph” or “graph data” is quite often used to refer, actually, to a system of relationships, which can be represented as a graph, rather than to the mathematical model itself. In line with this practice, the term “graph” is used in this chapter as a synonym to “system of relationships”. Graph data have high importance in many application domains, such as social network analysis, transport network analysis, systems biology, transaction analysis, to name a few. The relational nature of graph data sets presents unique challenges that open up space for distinctive and innovative visual analytics methods. Graphs often contain inherent hierarchies, and the analysis outcomes vary significantly depending on the scale (i.e., aggregation level) at which the relationships are considered. Identification, analysis, and interpretation of inherent structures, such as tightly connected parts of the network, requires a combination of algorithmic and interactive methods. Analysis of graph structure may require involvement of different graph theoretic properties of nodes and links, consideration of paths, and extraction of recurring connectivity patterns. All of these analyses are taken to a different level of complexity when these graphs are multivariate, i.e., where each node carries multiple attributes, and dynamic, i.e., when the structure of the graph changes with time. In this chapter, we discuss these challenges in depth and present examples of employing visual analytics techniques for extracting valuable findings from this highly rich and interesting data type.

Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-56146-8_7

Ordering information: This item can be ordered from
http://www.springer.com/9783030561468

DOI: 10.1007/978-3-030-56146-8_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().