Scalable Graph Size Reduction for Efficient GNN Application
The result's identifiers
Result code in 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>
Result on the web
<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
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Alternative languages
Result language
angličtina
Original language name
Scalable Graph Size Reduction for Efficient GNN Application
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 22nd Conference Information Technologies – Applications and Theory (ITAT 2022)
ISBN
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ISSN
1613-0073
e-ISSN
1613-0073
Number of pages
10
Pages from-to
75-84
Publisher name
CEUR-WS.org
Place of publication
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Event location
Zuberec
Event date
Sep 23, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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