Abstract
Graph databases are the technical implementation of graph theory, a concept that was introduced to applied mathematics about 200 years ago. Its versatile usability for various business and private applications fostered the establishment of a graph industry that markets graph databases, graph analysis tools, and graph frameworks. This article introduces the scientific foundations for graph theory and its realization in graph databases, and explains typical analysis tools and applications.
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Notes
- 1.
For an English translation see König and Tutte, Theory of Finite and Infinite Graphs (2013).
- 2.
In a directed graph, the edges connecting two different nodes have different meanings, depending on their direction. In an undirected graph, an edge connecting two nodes has a single meaning.
- 3.
This refers to a weighted graph, where an edge can be weighted to qualify its weight or strength, e.g., cost, length, distance.
- 4.
Example of Dijkstra algorithm: https://www.youtube.com/watch?v=UG7VmPWkJmA&feature=youtu.be&t=33.
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Bajer, K., Seidlitz, A., Steltgens, S., Wormuth, B. (2021). Graph Databases. In: Liermann, V., Stegmann, C. (eds) The Digital Journey of Banking and Insurance, Volume III. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-78821-6_3
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