Graph neural network framework for topology semantic dual-domain collaboration

doi:10.11772/j.issn.1001-9081.2025050566

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (5): 1378-1387.DOI: 10.11772/j.issn.1001-9081.2025050566

• Artificial intelligence • Previous Articles

Graph neural network framework for topology semantic dual-domain collaboration

Kun FU¹(), Haoyu WEI¹, Weijing LIU²^,³, Xing DANG²^,³, Zezheng LIU¹, Jianwei LI¹

^1.School of Artificial Intelligence，Hebei University of Technology，Tianjin 300401，China
^2.Tianjin Institute of Aerospace Mechanical and Electrical Equipment，Tianjin 300462，China
^3.Tianjin Enterprise Key Laboratory of Aerospace Intelligent Equipment Technology，Tianjin 300462，China

Received:2025-05-26 Revised:2025-08-30 Accepted:2025-09-01 Online:2025-09-15 Published:2026-05-10
Contact: Kun FU
About author:WEI Haoyu， born in 2000， M. S. candidate. His research interests include network representation learning.
LIU Weijing， born in 1990， M. S.， senior engineer. Her research interests include path optimization of AGV scheduling system.
DANG Xing， born in 1986， M. S.， senior engineer. Her research interests include intelligent manufacturing application system.
LIU Zezheng， born in 2000， M. S. candidate. His research interests include network representation learning.
LI Jianwei， born in 1974， Ph. D.， professor. His research interests include bioinformatics， graph convolutional neural networks.
Supported by:
National Natural Science Foundation of China(62072154);Key Science and Technology Program of Tianjin(22JCYBJC01740)

拓扑语义双域协同的图神经网络框架

富坤¹(), 魏昊宇¹, 刘伟静²^,³, 党兴²^,³, 刘泽政¹, 李建伟¹

^1.河北工业大学人工智能与数据科学学院，天津 300401
^2.天津航天机电设备研究所，天津 300462
^3.天津市宇航智能装备技术企业重点实验室，天津 300462

通讯作者: 富坤
作者简介:魏昊宇（2000—），男，河北保定人，硕士研究生，主要研究方向：网络表示学习
刘伟静（1990—），女，天津人，高级工程师，硕士，主要研究方向：自动导引车调度系统的路径优化
党兴（1986—），女，天津人，高级工程师，硕士，主要研究方向：智能制造应用系统
刘泽政（2000—），男，河北邢台人，硕士研究生，主要研究方向：网络表示学习
李建伟（1974—），男，河北唐山人，教授，博士，主要研究方向：生物信息学、图卷积神经网络。
基金资助:
国家自然科学基金资助项目(62072154);天津市科技计划项目(22JCYBJC01740)

Abstract

Abstract:

Graph Neural Network （GNN） commonly faces the Under?Reachability problem （Under?Reach） when processing graph data with sparse and unevenly distributed labeled nodes， which means that distant unlabeled nodes cannot effectively receive supervised signals due to topological constraints， leading to limited model generalization ability. Although existing methods can partially address this issue， they still have limitations， including over?smoothing， high computational complexity， and noise sensitivity. Therefore， a GNN framework for topology semantic dual?domain collaboration， named TriMix， was proposed to address the above challenges through three key improvements. First， a dynamic mixing ratio mechanism was designed to adjust mixing weights between pseudo?labels and ground?truth labels adaptively across training epochs， relying on ground?truth labels for stable convergence in the early stage while incorporating high?confidence pseudo?labels gradually in the latter?stage training for decision boundary expansion. Second， a topology?semantic dual?domain collaborative node?weighted sampling strategy was constructed by integrating node degree， PageRank value， and feature similarity， so as to quantify node importance and optimize information propagation paths， enhancing the reachability of low?centrality nodes. Third， a contrastive learning module was implemented with a triple?level negative sample generation strategy of category?driving， feature?similarity weighting， and pseudo?label guidance， to refine the discriminability between positive and negative samples in the embedding space， thereby enhancing the semantic understanding of unlabeled data. Experimental results on benchmark datasets such as Cora and PubMed showed that TriMix achieved node classification accuracy 2.1% to 4.4% higher than baseline models like Graph Convolutional Network （GCN） and Graph ATtention network （GAT）， with improved F1?score and generalization ability. The TriMix framework significantly improves learning efficiency on sparsely labeled graph data through dual?domain collaboration of topological structure and semantic features， providing a new approach to node classification tasks in complex graph structures.

Key words: Graph Neural Network (GNN), Under?Reachability Problem (Under?Reach), dynamic mixing, dual-domain collaboration, node weighted sampling, contrastive learning

摘要：

图神经网络（GNN）在处理标记节点稀疏且分布不均的图数据时，普遍面临欠可达问题（Under-Reach），即远端未标记节点因拓扑限制难以接收有效监督信号，导致模型泛化能力受限。现有方法虽能部分解决这一问题，但仍存在过度平滑、计算复杂度高、噪声敏感等局限性。因此，提出一种拓扑语义双域协同的图神经网络框架TriMix，通过三方面改进应对上述挑战：1）动态混合比例机制。基于训练轮次自适应调整伪标签与真实标签的混合权重，在训练初期依赖真实标签稳定收敛，后期逐步引入高置信伪标签以扩展决策边界。2）拓扑语义双域协同的节点加权采样策略。融合节点度、PageRank值和特征相似性，量化节点重要性并优化信息传播路径，提升低中心性节点的可达性。3）对比学习模块。通过类别驱动、特征相似性加权与伪标签引导的三级负样本生成策略，优化嵌入空间的正负样本区分度，增强模型对未标记数据的语义理解。在Cora、PubMed等经典数据集上的实验结果表明，TriMix的节点分类准确率比图卷积网络（GCN）、图注意力网络（GAT）等基线模型高2.1%~4.4%，F1评分与泛化能力均有所提升。TriMix框架通过拓扑结构与语义特征的双域协同优化显著提升了稀疏标记图数据的学习效率，为复杂图结构中的节点分类任务提供了新思路。

关键词: 图神经网络, 欠可达问题, 动态混合, 双域协同, 节点加权采样, 对比学习

CLC Number:

TP391

Kun FU, Haoyu WEI, Weijing LIU, Xing DANG, Zezheng LIU, Jianwei LI. Graph neural network framework for topology semantic dual-domain collaboration[J]. Journal of Computer Applications, 2026, 46(5): 1378-1387.

富坤, 魏昊宇, 刘伟静, 党兴, 刘泽政, 李建伟. 拓扑语义双域协同的图神经网络框架[J]. 《计算机应用》唯一官方网站, 2026, 46(5): 1378-1387.

Figures/Tables 14

Fig.1 Overall framework of TriMix

Tab. 1 Main symbols and related descriptions

符号	说明
G	输入的图数据
$A ∈ R n × n$	邻接矩阵
$X ∈ R n × f$	特征矩阵
n	图中节点数
f	每个节点的特征维度
$A i j$	A 中的第i与第j个节点间是否存在边，存在边取值为1，否则为0
$M ∈ R C × C$	类别分布矩阵
C	节点类别数
V	图数据中节点的集合

Tab. 1 Main symbols and related descriptions

符号	说明
G	输入的图数据
$A ∈ R n × n$	邻接矩阵
$X ∈ R n × f$	特征矩阵
n	图中节点数
f	每个节点的特征维度
$A i j$	A 中的第i与第j个节点间是否存在边，存在边取值为1，否则为0
$M ∈ R C × C$	类别分布矩阵
C	节点类别数
V	图数据中节点的集合

Fig. 2 Relationship between node reachability coefficient and node degree on Cora and PubMeddatasets

Tab. 2 Dataset information

数据集	领域	节点数	边数	特征维度	类别数
Cora^［13］	机器学习	2 708	5 429	1 433	7
PubMed^［14］	医学	19 717	44 338	500	3
CiteSeer^［20］	计算机科学	3 312	4 732	3 703	6
CS^［21］	计算机科学	18 333	81 894	6 805	15
Physics^［22］	物理	34 493	247 962	8 415	5
ogbn-arxiv^［23］	计算机科学	169 343	1 166 243	128	40
Reddit^［24］	社交网络	232 965	114 645 892	602	41

Tab. 3 Comparison of node classification accuracy （mean±standard deviation）

主干模型	增强方法	准确率（均值±标准差）
主干模型	增强方法	Cora	PubMed	CiteSeer	CS	Physics
GCN	original	81.57±0.40	77.91±0.30	70.50±0.67	91.24±0.40	92.56±1.30
	UPS	82.35±0.40	78.45±0.40	72.82±0.60	91.62±0.30	93.01±0.30
	PASTEL	81.97±0.60	78.92±0.29	71.32±0.40	91.76±0.60	>3days
	ReNode	81.97±0.60	78.13±0.70	69.48±0.40	91.32±0.10	OOM
	GraphMix	82.29±3.70	82.82±0.50	74.55±0.50	91.90±0.20	90.43±1.70
	NodeMixup	83.47±0.30	81.16±0.20	74.12±0.30	92.69±0.40	93.97±0.40
	HDS-GNN	84.60±0.10	79.90±0.20	73.00±0.20	93.80±0.20	—
	TriMix	83.58±0.17	81.80±0.36	74.89±0.40	92.55±0.47	94.64±0.10
GAT	original	82.04±0.60	78.00±0.70	71.82±0.80	90.52±0.40	91.97±0.60
	UPS	82.17±0.50	78.56±0.90	72.97±0.70	91.26±0.40	92.45±1.10
	PASTEL	82.21±0.30	78.74±0.90	72.35±0.80	90.31±0.20	>3days
	ReNode	81.88±0.70	79.68±0.50	71.73±1.20	88.36±0.50	OOM
	GraphMix	82.76±0.60	78.82±0.40	73.04±0.50	90.57±1.00	92.90±0.40
	NodeMixup	83.52±0.30	81.26±0.30	74.30±0.10	92.69±0.20	93.87±0.30
	HDS	84.30±0.10	80.60±0.30	72.10±0.20	93.50±0.10	—
	TriMix	82.78± 0.75	81.84± 0.85	73.15±0.96	92.77±0.30	93.91±0.20
APPNP	original	80.03±0.50	78.67±0.20	70.30±0.60	91.79±0.50	92.36±0.80
	UPS	81.24±0.60	78.69±0.70	71.02±0.70	91.77±0.30	92.31±0.50
	PASTEL	81.56±0.30	78.39±0.20	70.68±0.80	91.98±0.40	>3days
	ReNode	81.12±0.20	78.58±0.30	70.04±0.80	91.99±0.20	OOM
	GraphMix	82.98±0.40	78.73±0.40	70.26±0.40	91.53±0.60	94.12±0.10
	NodeMixup	83.54±0.40	79.93±0.10	75.12±0.30	92.82±0.20	94.34±0.20
	EE-APPNP	81.48±0.47	78.90±0.52	71.45±0.54	—	—
	TriMix	83.60±0.65	78.24±0.59	71.55±0.40	92.88±0.70	94.63±0.30
GraphSAGE	original	78.12±0.30	77.30±0.70	68.09±0.80	91.01±0.90	93.09±0.40
	UPS	81.83±0.30	77.82±0.60	70.29±0.60	91.35±0.40	93.20±0.40
	PASTEL	78.58±0.60	78.26±0.70	70.31±0.30	91.77±0.60	>3 days
	ReNode	76.48±1.00	78.67±1.20	70.79±0.90	89.61±0.70	OOM
	GraphMix	80.09±0.80	79.85±0.40	70.97±1.20	91.55±0.30	93.25±0.30
	GraphSANN	77.73±0.75	66.39±0.15	75.54±1.12	89.12±0.28	92.73±1.02
	NodeMixup	81.93±0.20	79.97±0.50	74.12±0.40	91.97±0.20	94.76±0.20
	TriMix	82.56±0.28	79.25±0.05	72.14±0.06	92.34±0.40	95.12±0.20

Tab. 3 Comparison of node classification accuracy （mean±standard deviation）

主干模型	增强方法	准确率（均值±标准差）
主干模型	增强方法	Cora	PubMed	CiteSeer	CS	Physics
GCN	original	81.57±0.40	77.91±0.30	70.50±0.67	91.24±0.40	92.56±1.30
	UPS	82.35±0.40	78.45±0.40	72.82±0.60	91.62±0.30	93.01±0.30
	PASTEL	81.97±0.60	78.92±0.29	71.32±0.40	91.76±0.60	>3days
	ReNode	81.97±0.60	78.13±0.70	69.48±0.40	91.32±0.10	OOM
	GraphMix	82.29±3.70	82.82±0.50	74.55±0.50	91.90±0.20	90.43±1.70
	NodeMixup	83.47±0.30	81.16±0.20	74.12±0.30	92.69±0.40	93.97±0.40
	HDS-GNN	84.60±0.10	79.90±0.20	73.00±0.20	93.80±0.20	—
	TriMix	83.58±0.17	81.80±0.36	74.89±0.40	92.55±0.47	94.64±0.10
GAT	original	82.04±0.60	78.00±0.70	71.82±0.80	90.52±0.40	91.97±0.60
	UPS	82.17±0.50	78.56±0.90	72.97±0.70	91.26±0.40	92.45±1.10
	PASTEL	82.21±0.30	78.74±0.90	72.35±0.80	90.31±0.20	>3days
	ReNode	81.88±0.70	79.68±0.50	71.73±1.20	88.36±0.50	OOM
	GraphMix	82.76±0.60	78.82±0.40	73.04±0.50	90.57±1.00	92.90±0.40
	NodeMixup	83.52±0.30	81.26±0.30	74.30±0.10	92.69±0.20	93.87±0.30
	HDS	84.30±0.10	80.60±0.30	72.10±0.20	93.50±0.10	—
	TriMix	82.78± 0.75	81.84± 0.85	73.15±0.96	92.77±0.30	93.91±0.20
APPNP	original	80.03±0.50	78.67±0.20	70.30±0.60	91.79±0.50	92.36±0.80
	UPS	81.24±0.60	78.69±0.70	71.02±0.70	91.77±0.30	92.31±0.50
	PASTEL	81.56±0.30	78.39±0.20	70.68±0.80	91.98±0.40	>3days
	ReNode	81.12±0.20	78.58±0.30	70.04±0.80	91.99±0.20	OOM
	GraphMix	82.98±0.40	78.73±0.40	70.26±0.40	91.53±0.60	94.12±0.10
	NodeMixup	83.54±0.40	79.93±0.10	75.12±0.30	92.82±0.20	94.34±0.20
	EE-APPNP	81.48±0.47	78.90±0.52	71.45±0.54	—	—
	TriMix	83.60±0.65	78.24±0.59	71.55±0.40	92.88±0.70	94.63±0.30
GraphSAGE	original	78.12±0.30	77.30±0.70	68.09±0.80	91.01±0.90	93.09±0.40
	UPS	81.83±0.30	77.82±0.60	70.29±0.60	91.35±0.40	93.20±0.40
	PASTEL	78.58±0.60	78.26±0.70	70.31±0.30	91.77±0.60	>3 days
	ReNode	76.48±1.00	78.67±1.20	70.79±0.90	89.61±0.70	OOM
	GraphMix	80.09±0.80	79.85±0.40	70.97±1.20	91.55±0.30	93.25±0.30
	GraphSANN	77.73±0.75	66.39±0.15	75.54±1.12	89.12±0.28	92.73±1.02
	NodeMixup	81.93±0.20	79.97±0.50	74.12±0.40	91.97±0.20	94.76±0.20
	TriMix	82.56±0.28	79.25±0.05	72.14±0.06	92.34±0.40	95.12±0.20

Tab. 4 Comparison of node classification accuracy on large datasets

模型	ogbn-arxiv	Reddit
GCN	59.30±1.20	89.70±1.00
TDGIA	57.00±0.10	86.11±0.10
GCond	59.20±0.10	88.00±1.80
GCond-X	61.30±0.50	88.40±0.40
DC-GCN	64.07±0.12	90.37±0.27
BS-KAGCN	53.76±0.31	87.23±0.26
TriMix-GCN	64.71±0.46	89.25±0.60

Tab. 5 Comparison of model F1-score

模型	策略	Cora	CiteSeer	PubMed
GCN	original	81.6±0.7	71.6±0.4	70.3±0.2
	BGCN	81.2±0.8	72.4±0.5	71.6±0.3
	AdaEdge	81.9±0.7	72.8±0.7	72.1±0.6
	DropEdge	82.0±0.8	71.8±0.2	74.3±0.5
	TriMix	82.6±0.2	73.0±0.3	77.4±0.4
GAT	original	81.3±1.1	70.5±0.7	69.4±0.7
	BGCN	80.8±0.8	70.8±0.6	71.1±0.2
	AdaEdge	82.0±0.6	71.1±0.8	73.5±0.4
	DropEdge	81.9±0.6	71.0±0.5	73.2±0.3
	TriMix	82.2±0.7	71.6±0.4	76.3±0.4

Tab. 6 Comparison of link prediction accuracy

模型	链路预测准确率	模型	链路预测准确率
GAT	81.7±0.2	CorGCN	87.1±0.2
GCN	82.9±0.1	TriMix-GCN	87.9±0.3
NodeMixup	85.6±0.6

Tab. 7 Accuracy of node classification in ablation experiments

模型与变体	Cora	PubMed	CiteSeer
TriMix	83.58	81.80	74.89
TriMix-A	81.77	77.63	70.33
TriMix-B	81.66	77.55	69.62
TriMix-C	82.05	78.63	69.49

Fig. 3 Accuracy variation under impact of single parameter

Fig. 4 Classification accuracy of two-parameter combinations

Fig. 5 Classification accuracy of three-parameter combinations

Tab.8 Comparison of model training efficiency

模型	参数量/10⁶	峰值内存占用/GB	每epoch训练时间/ms
GCN	0.89	0.65	323
GAT	1.37	1.08	668
NodeMixup	0.91	0.87	449
GPM	0.81	1.60	196
TriMix-GCN	1.16	1.03	463
TriMix-GAT	1.41	1.37	695

Fig. 6 Visualization results for node classification

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Graph neural network framework for topology semantic dual-domain collaboration

拓扑语义双域协同的图神经网络框架

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