Descriptive vs. Inferential Community Detection in Networks: Pitfalls, Myths and Half-Truths - Brossura
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This Element closes the gap between the state-of-the-art in community detection on networks and the methods actually used in practice.
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Descriptive vs. Inferential Community Detection in Networks (Paperback)
Editore:
Cambridge University Press, Cambridge, 2023
ISBN 10: 1009113003
ISBN 13: 9781009113007
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Paperback. Condizione: new. Paperback. 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. 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 Element closes the gap between the state-of-the-art in community detection on networks and the methods actually used in practice. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781009113007
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Descriptive vs. Inferential Community Detection in Networks (Elements in the Structure and Dynamics of Complex Networks)
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Descriptive vs. Inferential Community Detection in Networks: Pitfalls, Myths and Half-Truths
Editore:
Cambridge University Press, 2023
ISBN 10: 1009113003
ISBN 13: 9781009113007
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Descriptive vs. Inferential Community Detection in Networks (Elements in the Structure and Dynamics of Complex Networks)
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Descriptive vs. Inferential Community Detection in Networks: Pitfalls, Myths and Half-Truths
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Cambridge University Press, 2023
ISBN 10: 1009113003
ISBN 13: 9781009113007
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Descriptive vs. Inferential Community Detection in Networks (Elements in the Structure and Dynamics of Complex Networks)
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Descriptive vs. Inferential Community Detection in Networks: Pitfalls, Myths and Half-Truths
Editore:
Cambridge University Press, 2023
ISBN 10: 1009113003
ISBN 13: 9781009113007
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Da: Revaluation Books, Exeter, Regno Unito
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Paperback. Condizione: Brand New. 75 pages. 9.00x6.00x0.19 inches. In Stock. Codice articolo x-1009113003
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Descriptive vs. Inferential Community Detection in Networks (Elements in the Structure and Dynamics of Complex Networks)
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Descriptive vs. Inferential Community Detection in Networks (Paperback)
Editore:
Cambridge University Press, Cambridge, 2023
ISBN 10: 1009113003
ISBN 13: 9781009113007
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Paperback. Condizione: new. Paperback. 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. 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 Element closes the gap between the state-of-the-art in community detection on networks and the methods actually used in practice. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9781009113007
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