Node Classification Based on Non-symmetric Dependencies and Graph Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254686" target="_blank" >RIV/61989100:27240/23:10254686 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-21131-7_27" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-21131-7_27</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-21131-7_27" target="_blank" >10.1007/978-3-031-21131-7_27</a>
Alternative languages
Result language
angličtina
Original language name
Node Classification Based on Non-symmetric Dependencies and Graph Neural Networks
Original language description
One of the interesting tasks in social network analysis is detecting network nodes' roles in their interactions. The first problem is discovering such roles, and the second is detecting the discovered roles in the network. Role detection, i.e., assigning a role to a node, is a classification task. Our paper addresses the second problem and uses three roles (classes) for classification. These roles are based only on the structural properties of the neighborhood of a given node and use the previously published non-symmetric relationship between pairs of nodes for their definition. This paper presents transductive learning experiments using graph neural networks (GNN) to show that excellent results can be obtained even with a relatively small sample size for training the network.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2
ISBN
978-3-031-21133-1
ISSN
1860-949X
e-ISSN
1860-9503
Number of pages
11
Pages from-to
347-357
Publisher name
SPRINGER INTERNATIONAL PUBLISHING AG
Place of publication
CHAM
Event location
Univ Palermo
Event date
Nov 8, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000963499200027