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The Effect of Telework Frequency on Communication Media and Knowledge Sharing in Japan Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Agent-Based Vector-Label Propagation for Explaining Social Network Structures

Authors : Valerio Bellandi, Paolo Ceravolo, Ernesto Damiani, Samira Maghool

Published in: Knowledge Management in Organisations

Publisher: Springer International Publishing

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Abstract

Even though Social Network Analysis is quite helpful in studying the structural properties of interconnected systems, real-world networks reveal much more hidden characteristics from interacting domain-specific features. In this study, we designed an Agent-based Vector-label PRopagation Algorithm (AVPRA), which captures both structural properties and domain-specific features of a given network by assigning vectors of features to constituting agents. Experimental analysis proves that our algorithm is accurate in revealing the structural properties of a network in an explainable fashion. Furthermore, the resulting vector-labels are suitable for downstream machine learning tasks.

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Footnotes
1
The networks are created using the powerlaw_cluster_graph function of NetworkX package 2, which is based on Holme and Kim algorithm [18].
 
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Metadata
Title
Agent-Based Vector-Label Propagation for Explaining Social Network Structures
Authors
Valerio Bellandi
Paolo Ceravolo
Ernesto Damiani
Samira Maghool
Copyright Year
2022
Book
Knowledge Management in Organisations

Print ISBN: 978-3-031-07919-1

Electronic ISBN: 978-3-031-07920-7

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-031-07920-7_24

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