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Sign-Aware Graph Learning Framework for Stance Detection

delete2025-01-01
delete0
PRE
AI
C
Chao Meng
张
张红梨 (Hongli Zhang)
G
Gongzhu Yin
Y
Yang Gao
B
Binxing Fang
DOI：10.1109/LSP.2025.3588084delete
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摘要

摘要

En 中文
立场检测旨在识别个体对特定目标的态度。链接的符号在各类图相关任务中具有积极作用。然而，现有的立场检测方法忽视了这一信息。这是因为许多现实世界社交网络中的链接符号并未明确提供，导致当前的符号网络表示学习方法无法应用于此场景。本研究提出了一种符号感知图学习框架（SAGL），用于立场检测，通过创新性地设计了符号推断图自编码器（SIGAE）和平衡注入符号图注意力网络（BSGAN）。SIGAE利用立场标签和底层结构信息，从无符号网络中推断并生成符号链接。BSGAN将平衡理论引入符号图注意力网络，并通过捕捉和动态调整符号链接的影响来优化节点表示。在真实数据集上的大量实验表明，SAGL显著优于竞争基线方法。
Keyword:
Stance detection
graph neural networks
signed networks
balance theory

期刊

IEEE Signal Processing Magazine 封面图
IEEE Signal Processing Magazine
IF:
9.6
论文数:
1.1W
被引数:
1.7W

机构

H
harbin institute of technology
学者数:
8.0W
论文数: 6.6W
被引数: 66
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