Node Classification Based on Non-symmetric Dependencies and Graph Neural Networks

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Node Classification Based on Non-symmetric Dependencies and Graph Neural Networks

Popis výsledku v původním jazyce

One of the interesting tasks in social network analysis is detecting network nodes&apos; 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.

Název v anglickém jazyce

Node Classification Based on Non-symmetric Dependencies and Graph Neural Networks

Popis výsledku anglicky

One of the interesting tasks in social network analysis is detecting network nodes&apos; 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2

ISBN

978-3-031-21133-1

ISSN

1860-949X

e-ISSN

1860-9503

Počet stran výsledku

11

Strana od-do

347-357

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

Univ Palermo

Datum konání akce

8. 11. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000963499200027