Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract)

Yao Cheng; Minjie Chen; Caihua Shan; Xiang Li

doi:10.1109/ICDE65448.2025.00396

Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract)

Yao Cheng
, Minjie Chen
, Caihua Shan
, Xiang Li

School of Data Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Graphs are ubiquitous in the real world, in graphs, nodes represent entities and edges capture their relationships. Recently, graph neural networks (GNNs) [3]-[6] have been proposed to integrate these two sources of information. In GNNs, a node's embedding is learned by aggregating messages from its neighbors.

Original language	English
Title of host publication	Proceedings - 2025 IEEE 41st International Conference on Data Engineering, ICDE 2025
Publisher	IEEE Computer Society
Pages	4734-4735
Number of pages	2
ISBN (Electronic)	9798331536039
DOIs	https://doi.org/10.1109/ICDE65448.2025.00396
State	Published - 2025
Event	41st IEEE International Conference on Data Engineering, ICDE 2025 - Hong Kong, China Duration: 19 May 2025 → 23 May 2025

Publication series

Name	Proceedings - International Conference on Data Engineering
ISSN (Print)	1084-4627
ISSN (Electronic)	2375-0286

Conference

Conference	41st IEEE International Conference on Data Engineering, ICDE 2025
Country/Territory	China
City	Hong Kong
Period	19/05/25 → 23/05/25

Keywords

classification
graph heterophily
graph neural networks
representation learning

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10.1109/ICDE65448.2025.00396

Cite this

Cheng, Y., Chen, M., Shan, C., & Li, X. (2025). Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract). In Proceedings - 2025 IEEE 41st International Conference on Data Engineering, ICDE 2025 (pp. 4734-4735). (Proceedings - International Conference on Data Engineering). IEEE Computer Society. https://doi.org/10.1109/ICDE65448.2025.00396

@inproceedings{04648782762742e997a6a432465c688d,

title = "Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract)",

abstract = "Graphs are ubiquitous in the real world, in graphs, nodes represent entities and edges capture their relationships. Recently, graph neural networks (GNNs) [3]-[6] have been proposed to integrate these two sources of information. In GNNs, a node's embedding is learned by aggregating messages from its neighbors.",

keywords = "classification, graph heterophily, graph neural networks, representation learning",

author = "Yao Cheng and Minjie Chen and Caihua Shan and Xiang Li",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 41st IEEE International Conference on Data Engineering, ICDE 2025 ; Conference date: 19-05-2025 Through 23-05-2025",

year = "2025",

doi = "10.1109/ICDE65448.2025.00396",

language = "英语",

series = "Proceedings - International Conference on Data Engineering",

publisher = "IEEE Computer Society",

pages = "4734--4735",

booktitle = "Proceedings - 2025 IEEE 41st International Conference on Data Engineering, ICDE 2025",

address = "美国",

}

Cheng, Y, Chen, M, Shan, C & Li, X 2025, Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract). in Proceedings - 2025 IEEE 41st International Conference on Data Engineering, ICDE 2025. Proceedings - International Conference on Data Engineering, IEEE Computer Society, pp. 4734-4735, 41st IEEE International Conference on Data Engineering, ICDE 2025, Hong Kong, China, 19/05/25. https://doi.org/10.1109/ICDE65448.2025.00396

Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract). / Cheng, Yao; Chen, Minjie; Shan, Caihua et al.
Proceedings - 2025 IEEE 41st International Conference on Data Engineering, ICDE 2025. IEEE Computer Society, 2025. p. 4734-4735 (Proceedings - International Conference on Data Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract)

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AU - Shan, Caihua

AU - Li, Xiang

PY - 2025

Y1 - 2025

N2 - Graphs are ubiquitous in the real world, in graphs, nodes represent entities and edges capture their relationships. Recently, graph neural networks (GNNs) [3]-[6] have been proposed to integrate these two sources of information. In GNNs, a node's embedding is learned by aggregating messages from its neighbors.

AB - Graphs are ubiquitous in the real world, in graphs, nodes represent entities and edges capture their relationships. Recently, graph neural networks (GNNs) [3]-[6] have been proposed to integrate these two sources of information. In GNNs, a node's embedding is learned by aggregating messages from its neighbors.

KW - classification

KW - graph heterophily

KW - graph neural networks

KW - representation learning

UR - https://www.scopus.com/pages/publications/105015424390

U2 - 10.1109/ICDE65448.2025.00396

DO - 10.1109/ICDE65448.2025.00396

M3 - 会议稿件

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EP - 4735

BT - Proceedings - 2025 IEEE 41st International Conference on Data Engineering, ICDE 2025

PB - IEEE Computer Society

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Y2 - 19 May 2025 through 23 May 2025

ER -

Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract)

Abstract

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Keywords

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