Prediction of Urban Population-Facilities Interactions with Graph Neural Network

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of Urban Population-Facilities Interactions with Graph Neural Network

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Prediction of Urban Population-Facilities Interactions with Graph Neural Network

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>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

Computational Science and Its Applications – ICCSA 2023 : Lecture Notes in Computer Science, vol 13956

ISBN

9783031368042

ISSN

—

e-ISSN

—

Počet stran výsledku

15

Strana od-do

334-348

Název nakladatele

Springer, Cham

Místo vydání

Cham

Místo konání akce

Athens

Datum konání akce

3. 7. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001166618800023