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  5. Modelling Weighted Signed Networks
 
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Modelling Weighted Signed Networks

Author(s)
Caimo, Alberto  
Gollini, Isabella  
Uri
http://hdl.handle.net/10197/10926
Date Issued
2019-06-21
Date Available
2019-07-18T11:02:48Z
Abstract
In this paper we introduce a new modelling approach to analyse weighted signed networks by assuming that their generative process consists of two models: the interaction model which describes the overall connectivity structure of the relations in the network without taking into account neither the weight nor the sign of the dyadic relations; and the conditional weighted signed network model describes how the edge signed weights form given the interaction structure. We then show how this modelling approach can facilitate the interpretation of the overall network process. Finally, we adopt a Bayesian inferential approach to illustrate the new methodology by modelling the Sampson’s influence network.
Type of Material
Conference Publication
Publisher
Pearson
Subjects

Signed networks

Weighted networks

Exponential-family ne...

Bayesian inference

Web versions
https://mathesia.com/sis19/
Language
English
Italian
Status of Item
Peer reviewed
Journal
Arbia, G., Peluso, S., Pini, A. & Rivellini, G. (eds.). Smart Statistics for Smart Applications, Book of Short Papers SIS 2019
Conference Details
SIS 2019: Conference of the Italian Statistical Society Milan, 18-21 June 2019
ISBN
9788891915108
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Caimo_Gollini_2019_SIS.pdf

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Owning collection
Mathematics and Statistics Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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