Descriptive vs. Inferential Community Detection in Networks
Pitfalls, Myths and Half-Truths
Published on 23. August 2023
978-1-009-12233-7 (ISBN)
Description
Community detection is one of the most important methodological fields of network science, and one which has attracted a significant amount of attention over the past decades. This area deals with the automated division of a network into fundamental building blocks, with the objective of providing a summary of its large-scale structure. Despite its importance and widespread adoption, there is a noticeable gap between what is arguably the state-of-the-art and the methods which are actually used in practice in a variety of fields. The Elements attempts to address this discrepancy by dividing existing methods according to whether they have a 'descriptive' or an 'inferential' goal. While descriptive methods find patterns in networks based on context-dependent notions of community structure, inferential methods articulate a precise generative model, and attempt to fit it to data. In this way, they are able to provide insights into formation mechanisms and separate structure from noise. This title is also available as open access on Cambridge Core.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Illustrations
Worked examples or Exercises
File size
27,52 MB
ISBN-13
978-1-009-12233-7 (9781009122337)
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Schweitzer Classification
Other editions
Additional editions
Tiago P. Peixoto
Descriptive vs. Inferential Community Detection in Networks
Pitfalls, Myths and Half-Truths
Book
08/2023
Cambridge University Press
€24.00
Shipment within 15-20 days
Person
Content
1. Introduction; 2. Descriptive vs. inferential community detection; 3. Modularity maximization considered harmful; 4. Myths, pitfalls, and half-truths; 5. Conclusion; References.
