Prediction of Urban Population-Facilities Interactions with Graph Neural Network

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00133441" target="_blank" >RIV/00216224:14310/23:00133441 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-36805-9_23" target="_blank" >https://doi.org/10.1007/978-3-031-36805-9_23</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-36805-9_23" target="_blank" >10.1007/978-3-031-36805-9_23</a>

Result language
angličtina
Original language name
Prediction of Urban Population-Facilities Interactions with Graph Neural Network
Original language description
The urban population interacts with service facilities on a daily basis. The information on population-facilities interactions is considered when analyzing the current city organization and revealing gaps in infrastructure at the neighborhood level. However, often this information is limited to several observation areas. The paper presents a new graph-based deep learning approach to reconstruct population-facilities interactions. In the proposed approach, graph attention neural networks learn latent nodes’ representation and discover interpretable dependencies in a graph of interactions based on observed data of one part of the city. A novel normalization technique is used to balance doubly-constrained flows between two locations. The experiments show that the proposed approach outperforms classic models in a bipartite graph of population-facilities interactions.
Czech name
—
Czech description
—

Project
<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Computational Science and Its Applications – ICCSA 2023 : Lecture Notes in Computer Science, vol 13956
ISBN
9783031368042
ISSN
—
e-ISSN
—
Number of pages
15
Pages from-to
334-348
Publisher name
Springer, Cham
Place of publication
Cham
Event location
Athens
Event date
Jul 3, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001166618800023

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