[1]郑帅,彭奏章,朱振峰,等.多双曲空间传递图解耦表示学习[J].智能系统学报,2025,20(5):1243-1255.[doi:10.11992/tis.202409034]
ZHENG Shuai,PENG Zouzhang,ZHU Zhenfeng,et al.Graph disentanglement representation learning based on propagation in multiple hyperbolic spaces[J].CAAI Transactions on Intelligent Systems,2025,20(5):1243-1255.[doi:10.11992/tis.202409034]
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ZHENG Shuai,PENG Zouzhang,ZHU Zhenfeng,et al.Graph disentanglement representation learning based on propagation in multiple hyperbolic spaces[J].CAAI Transactions on Intelligent Systems,2025,20(5):1243-1255.[doi:10.11992/tis.202409034]
多双曲空间传递图解耦表示学习
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第5期
页码:
1243-1255
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2025-09-05
- Title:
- Graph disentanglement representation learning based on propagation in multiple hyperbolic spaces
- Keywords:
- graph representation learning; graph disentanglement; hyperbolic space; graph neural networks; label propagation; mixture of experts; topology refinement; multiresolution
- 分类号:
- TP18
- 摘要:
- 现有的图表示学习中存在两个问题，一是缺乏对细粒度邻域建模的考量，忽略了邻域节点间纠缠的多重语义；二是图表示学习的空间度量问题，欧氏空间并非是度量节点表示的最优选择。为解决上述问题，提出一种多双曲空间下表征传递预测的全新架构，实现多双曲空间度量下的图解耦表示学习。在该架构下，通过节点表征将原始拓扑结构映射至双曲空间，获得双曲空间下的多分辨率传递矩阵。进一步地，基于混合专家结构设计，视不同分辨率的双曲标签传递网络为专家网络，从而发现由不同潜在因素引发的节点连接模式。在多个真实世界的数据集上的实验结果显示，本文方法在Squirrel和Crocodile数据集上分别达到32.3%和59.5%的分类准确率，可视化实验进一步证明了方法的有效性。
- Abstract:
- There are two salient issues of existing graph representation learning methods. First, there is a dearth of fine-grained neighborhood modeling, which neglects the multifaceted semantic entanglements in the neighborhood structures. Second, the spatial metric employed in graph representation learning presents a significant challenge, since Euclidean space may not constitute the optimal framework for quantifying node representations. To solve these challenges, this study proposes a novel representation propagation and prediction mechanism within multiple hyperbolic spaces, thereby achieving disentangled graph representation learning under multifaceted hyperbolic spatial metrics. Within the proposed framework, the original topological structure is iteratively refined through node representations, yielding propagation matrices embedded in a hyperbolic space. Furthermore, based on a mixture-of-experts design, hyperbolic label propagation networks at different resolutions are treated as expert networks, enabling the discovery of node connection patterns induced by different latent factors. Experimental results on multiple real-world datasets show that the proposed method achieves classification accuracies of 32.3% and 59.5% on the Squirrel and Crocodile datasets, respectively. Additionally, visualization experiments further demonstrate the effectiveness of the proposed approach.
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- 相似文献/References:
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备注/Memo
收稿日期:2024-9-26。
基金项目:中央高校基本科研业务费项目(2024XKRC088); 国家自然科学基金项目(62476022); 北京市自然科学基金青年科学基金项目(4254085).
作者简介:郑帅，高聘副教授，博士。主要研究方向为图表示学习、图神经网络和多模态表示学习。发表学术论文20余篇。E-mail：shuaizheng@bjtu.edu.cn。;彭奏章，硕士，主要研究方向为图分析与解耦图表示学习。E-mail：pengzouzhang@bjtu.edu.cn。;朱振峰，教授，博士生导师，主要研究方向为机器学习、计算机视觉和动态数据挖掘。主持科技创新2030—“新一代人工智能”重大项目、国家自然科学基金项目、北京市自然科学基金项目等。获第十三届吴文俊人工智能科学技术科技进步二等奖 (排名第1)。E-mail：zhfzhu@bjtu.edu.cn。
通讯作者:朱振峰. E-mail：zhfzhu@bjtu.edu.cn
更新日期/Last Update:
2025-09-05