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Erschienen in:
The Effect of Telework Frequency on Communication Media and Knowledge Sharing in Japan Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

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

verfasst von : Valerio Bellandi, Paolo Ceravolo, Ernesto Damiani, Samira Maghool

Erschienen in: Knowledge Management in Organisations

Verlag: 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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Vorheriges Kapitel Chatbots for News Delivery – Investigations into Intrinsic Motivation and User Engagement
Nächstes Kapitel An Asymmetric Parallel Residual Convolutional Neural Network for Pen-Holding Gesture Recognition
Fußnoten
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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Metadaten
Titel
Agent-Based Vector-Label Propagation for Explaining Social Network Structures
verfasst von
Valerio Bellandi
Paolo Ceravolo
Ernesto Damiani
Samira Maghool
Copyright-Jahr
2022
Buch
Knowledge Management in Organisations

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

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

Copyright-Jahr: 2022

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

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