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2024 | OriginalPaper | Buchkapitel

Classification Supported by Community-Aware Node Features

verfasst von : Bogumił Kamiński, Paweł Prałat, François Théberge, Sebastian Zając

Erschienen in: Complex Networks & Their Applications XII

Verlag: Springer Nature Switzerland

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Abstract

A community structure that is often present in complex networks plays an important role not only in their formation but also shapes dynamics of these networks, affecting properties of their nodes. In this paper, we propose a family of community-aware node features and then investigate their properties. We show that they have high predictive power for classification tasks. We also verify that they contain information that cannot be recovered completely neither by classical node features nor by classical or structural node embeddings.

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Vorheriges Kapitel Network Based Methodology for Characterizing Interdisciplinary Expertise in Emerging Research

Nächstes Kapitel Signature-Based Community Detection for Time Series

The preprint of the longer version can be found on-line: https://math.torontomu.ca/~pralat/research.html.

https://github.com/sebkaz/BetaStar.git.

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Titel: Classification Supported by Community-Aware Node Features
verfasst von: Bogumił Kamiński
Paweł Prałat
François Théberge
Sebastian Zając
Verlag: Springer Nature Switzerland
Buch: Complex Networks & Their Applications XII
Print ISBN: 978-3-031-53498-0

Electronic ISBN: 978-3-031-53499-7

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-53499-7_11

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