Scalable Graph Size Reduction for Efficient GNN Application

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F22%3A00362532" target="_blank" >RIV/68407700:21340/22:00362532 - isvavai.cz</a>

Výsledek na webu

<a href="https://ceur-ws.org/Vol-3226/paper9.pdf" target="_blank" >https://ceur-ws.org/Vol-3226/paper9.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Scalable Graph Size Reduction for Efficient GNN Application

Popis výsledku v původním jazyce

Graph neural networks (GNN) present a dominant framework for representation learning on graphs for the past several years. The main strength of GNNs lies in the fact that they can simultaneously learn from both node related attributes and relations between nodes, represented by edges. In tasks leading to large graphs, GNN often requires significant computational resources to achieve its superior performance. In order to reduce the computational cost, methods allowing for a flexible balance between complexity and performance could be useful. In this work, we propose a simple scalable task-aware graph preprocessing procedure allowing us to obtain a reduced graph such as GNN achieves a given desired performance on the downstream task. In addition, the proposed preprocessing allows for fitting the reduced graph and GNN into a given memory/computational resources. The proposed preprocessing is evaluated and compared with several reference scenarios on conventional GNN benchmark datasets.

Název v anglickém jazyce

Scalable Graph Size Reduction for Efficient GNN Application

Popis výsledku anglicky

Graph neural networks (GNN) present a dominant framework for representation learning on graphs for the past several years. The main strength of GNNs lies in the fact that they can simultaneously learn from both node related attributes and relations between nodes, represented by edges. In tasks leading to large graphs, GNN often requires significant computational resources to achieve its superior performance. In order to reduce the computational cost, methods allowing for a flexible balance between complexity and performance could be useful. In this work, we propose a simple scalable task-aware graph preprocessing procedure allowing us to obtain a reduced graph such as GNN achieves a given desired performance on the downstream task. In addition, the proposed preprocessing allows for fitting the reduced graph and GNN into a given memory/computational resources. The proposed preprocessing is evaluated and compared with several reference scenarios on conventional GNN benchmark datasets.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 22nd Conference Information Technologies – Applications and Theory (ITAT 2022)

ISBN

—

ISSN

1613-0073

e-ISSN

1613-0073

Počet stran výsledku

10

Strana od-do

75-84

Název nakladatele

CEUR-WS.org

Místo vydání

—

Místo konání akce

Zuberec

Datum konání akce

23. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—