IEEE Transactions on Knowledge and Data Engineering最新文献_第4页

Multi-Level Task-Agnostic Graph Representation Learning With Isomorphic-Consistent Variational Graph Auto-Encoders 基于同构一致变分图自编码器的多级任务不可知图表示学习

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-22 DOI: 10.1109/TKDE.2025.3591732

Hanxuan Yang;Qingchao Kong;Wenji Mao

{"title":"Multi-Level Task-Agnostic Graph Representation Learning With Isomorphic-Consistent Variational Graph Auto-Encoders","authors":"Hanxuan Yang;Qingchao Kong;Wenji Mao","doi":"10.1109/TKDE.2025.3591732","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3591732","url":null,"abstract":"Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. In practice, it is desirable to develop task-agnostic graph representation learning methods that are typically trained in an unsupervised manner. However, existing unsupervised graph models, represented by the variational graph auto-encoders (VGAEs), can only address node- and link-level tasks while manifesting poor generalizability on the more difficult graph-level tasks because they can only keep low-order <italic>isomorphic consistency within the subgraphs of one-hop neighborhoods. To overcome the limitations of existing methods, in this paper, we propose the Isomorphic-Consistent VGAE (IsoC-VGAE) for multi-level task-agnostic graph representation learning. We first devise an unsupervised decoding scheme to provide a theoretical guarantee of keeping the high-order isomorphic consistency within the VGAE framework. We then propose the Inverse Graph Neural Network (Inv-GNN) decoder as its intuitive realization, which trains the model via reconstructing the node embeddings and neighborhood distributions learned by the GNN encoder. Extensive experiments on multi-level graph learning tasks verify that our model achieves superior or comparable performance compared to both the state-of-the-art unsupervised methods and representative supervised methods with distinct advantages on the graph-level tasks.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6061-6074"},"PeriodicalIF":10.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Lightweight Learned Cardinality Estimation Model 一种轻量级学习基数估计模型

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-21 DOI: 10.1109/TKDE.2025.3591025

Yaoyu Zhu;Jintao Zhang;Guoliang Li;Jianhua Feng

{"title":"A Lightweight Learned Cardinality Estimation Model","authors":"Yaoyu Zhu;Jintao Zhang;Guoliang Li;Jianhua Feng","doi":"10.1109/TKDE.2025.3591025","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3591025","url":null,"abstract":"Cardinality estimation is a fundamental task in database management systems, aiming to predict query results accurately without executing the queries. However, existing techniques either achieve low estimation accuracy or take high inference latency. Simultaneously achieving high speed and accuracy becomes critical for the cardinality estimation problem. In this paper, we propose a novel data-driven approach called <italic>CoDe (Covering with Decompositions) to address this problem. <italic>CoDe employs the concept of covering design, which divides the table into multiple smaller, overlapping segments. For each segment, <italic>CoDe utilizes tensor decomposition to accurately model its data distribution. Moreover, <italic>CoDe introduces innovative algorithms to select the best-fitting distributions for each query, combining them to estimate the final result. By employing multiple models to approximate distributions, <italic>CoDe excels in effectively modeling discrete distributions and ensuring computational efficiency. Notably, experimental results show that our method represents a significant advancement in cardinality estimation, achieving state-of-the-art levels of both estimation accuracy and inference efficiency. Across various datasets, <italic>CoDe achieves absolute accuracy in estimating more than half of the queries.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"5719-5734"},"PeriodicalIF":10.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DVCAE: Semi-Supervised Dual Variational Cascade Autoencoders for Information Popularity Prediction 用于信息流行度预测的半监督对偶变分级联自编码器

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-21 DOI: 10.1109/TKDE.2025.3591395

Jiaxing Shang;Xueqi Jia;Xiaoquan Li;Fei Hao;Ruiyuan Li;Geyong Min

{"title":"DVCAE: Semi-Supervised Dual Variational Cascade Autoencoders for Information Popularity Prediction","authors":"Jiaxing Shang;Xueqi Jia;Xiaoquan Li;Fei Hao;Ruiyuan Li;Geyong Min","doi":"10.1109/TKDE.2025.3591395","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3591395","url":null,"abstract":"Predicting information popularity in social networks has become a central focus of network analysis. While recent advancements have been made, most existing approaches rely solely on the final cascade size as the primary supervision signal for model optimization. This narrow focus limits the model generalization ability, particularly when faced with highly heterogeneous cascades. Additionally, in real-world scenarios, obtaining detailed social relationships is challenging, complicating effective structural feature learning. To address these issues, this paper proposes a semi-supervised model called Dual Variational Cascade AutoEncoders (DVCAE), which leverages parallel structural and temporal variational autoencoders for enhanced feature learning and popularity prediction. The model first aggregates multiple cascades into a global interaction graph, enabling structural information sharing across cascades. Then, it applies sparse matrix factorization-based graph embedding and graph filtering techniques on global and local cascade graphs respectively, generating initial node embeddings that are insensitive to topological perturbations. After that, two parallel variational autoencoders are designed to generate hidden representations for structural and temporal features respectively, with two self-supervised reconstruction losses integrated into the prediction loss to enrich supervision signals. Extensive experiments conducted on three real-world datasets demonstrate that DVCAE outperforms state-of-the-art models in terms of prediction accuracy.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"5838-5851"},"PeriodicalIF":10.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Thinking on Context: Inductive Relation Prediction Guided by the Reasoning Ability of Large Language Models 语境思维：基于大语言模型推理能力的归纳关系预测

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-21 DOI: 10.1109/TKDE.2025.3591056

Xiaoshu Chen;Sihang Zhou;Ke Liang;Jiafei Wu;Xinwang Liu;Dongsheng Li;Kai Lu

{"title":"Thinking on Context: Inductive Relation Prediction Guided by the Reasoning Ability of Large Language Models","authors":"Xiaoshu Chen;Sihang Zhou;Ke Liang;Jiafei Wu;Xinwang Liu;Dongsheng Li;Kai Lu","doi":"10.1109/TKDE.2025.3591056","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3591056","url":null,"abstract":"Inductive relation prediction aims to predict missing connections between entities unseen during training. Recent approaches adopt binary (positive or negative) training labels, which indicate whether the query relation exists between the entities, as supervision to teach models recognizing the entity-independent relation patterns in the context (enclosed subgraph or connective path). However, we argue that in this kind of method, the trained models are guided to make relation predictions by remembering whether the query relation and its contextual relational pattern co-occur more frequently in positive or negative samples. This solution could introduce two major limitations: 1) the model struggles with long-tail combinations, i.e., the combination between query relation and the relational pattern rarely occurs during training; 2) when noisy relational patterns, which fail to provide evidence for predicting the query relation, frequently occur with the query relation in positive training samples, the model will be misled into considering the noisy relational patterns as a feature supporting the existence of the query relation. To solve these problems, we propose ToC (Thinking on Context). ToC first utilizes large language models (LLMs) to incorporate a chain of thought as an additional supervisory constraint, guiding the model to make relational predictions based on logical reasoning instead of co-occurrence frequency. Additionally, ToC employs the reasoning capabilities of LLMs to construct context-level negative samples, aiding the model in identifying and disregarding noisy relational patterns. Extensive experiments show that ToC significantly outperforms state-of-the-art methods across three widely used datasets in multiple inductive settings.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6202-6215"},"PeriodicalIF":10.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simplified Graph Contrastive Learning Model Without Augmentation 无增强的简化图对比学习模型

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-18 DOI: 10.1109/TKDE.2025.3590482

Yuena Lin;Gengyu Lyu;Haichun Cai;Deng-Bao Wang;Haobo Wang;Zhen Yang

{"title":"Simplified Graph Contrastive Learning Model Without Augmentation","authors":"Yuena Lin;Gengyu Lyu;Haichun Cai;Deng-Bao Wang;Haobo Wang;Zhen Yang","doi":"10.1109/TKDE.2025.3590482","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3590482","url":null,"abstract":"Burgeoning graph contrastive learning (GCL) stands out in the graph domain with low annotated costs and high model performance improvements, which is typically composed of three standard configurations: 1) graph data augmentation (GraphDA), 2) multi-branch graph neural network (GNN) encoders and projection heads, 3) and contrastive loss. Unfortunately, the diverse GraphDA may corrupt graph semantics to different extents and meanwhile greatly burdens the time complexity on hyperparameter search. Besides, the multi-branch contrastive framework also demands considerable training consumption on encoding and projecting. In this paper, we propose one simplified GCL model to simultaneously address these problems via the minimal components of a general graph contrastive framework, i.e., a GNN encoder and a projection head. The proposed model treats the node representations generated by the GNN encoder and the projection head as positive pairs while considering all other representations as negatives, which not only liberates the model from the dependency on GraphDA but also streamlines the traditional multi-branch contrastive learning framework into a more efficient single-streamlined one. Through the in-depth theoretical analysis on the objective function, the mystery of why the proposed model works is illustrated. Empirical experiments on multiple public datasets demonstrate that the proposed model still ensures performance to be comparative with current advanced self-supervised GNNs.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6159-6172"},"PeriodicalIF":10.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge Graph-Based Patent Clustering 基于知识图的专利聚类

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-18 DOI: 10.1109/TKDE.2025.3590406

Pei-Yuan Lai;Man-Sheng Chen;Qing-Yun Dai;Chang-Dong Wang;Min Chen;Mohsen Guizani

{"title":"Knowledge Graph-Based Patent Clustering","authors":"Pei-Yuan Lai;Man-Sheng Chen;Qing-Yun Dai;Chang-Dong Wang;Min Chen;Mohsen Guizani","doi":"10.1109/TKDE.2025.3590406","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3590406","url":null,"abstract":"Patent data generally includes information from different perspectives or different types, and its heterogeneous attributes can be greatly beneficial to data clustering analysis. However, the existing patent analysis method always focus on the patent text cues, and such a strategy merely depends on the feature information to capture the data characteristics, failing to multi-type informative patent representation. Therefore, in this paper, to model the underlying structure/relationships of patent data, we employ the knowledge graph to depict the heterogeneous attributes of patent, and propose a novel Knowledge Graph-based Patent Clustering (KGPC) method, where the relationship reconstruction in knowledge graph as well as clustering-oriented representation refinement for patent clustering are jointly considered. With this model, there are three components, i.e., entity representation refinement, relationship reconstruction and self-supervised entity clustering. Given a patent knowledge graph as input, the entity representation refinement can be mutually boosted by the relationship reconstruction and self-supervised clustering objective, thereby leading to a balanced clustering-oriented output. Extensive experiments on several real-world patent knowledge graph datasets validate the effectiveness of KGPC while compared with the state-of-the-art.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6009-6019"},"PeriodicalIF":10.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Auto-Encoding Neural Tucker Factorization 自编码神经塔克分解

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-17 DOI: 10.1109/TKDE.2025.3590198

Peng Tang;Xin Luo;Jim Woodcock

{"title":"Auto-Encoding Neural Tucker Factorization","authors":"Peng Tang;Xin Luo;Jim Woodcock","doi":"10.1109/TKDE.2025.3590198","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3590198","url":null,"abstract":"Low-rank latent factorization of tensors is a powerful method for analyzing high-dimensional and incomplete (HDI) data derived from cyber-physical systems, particularly when computational resources are limited. However, traditional tensor factorization models are inherently linear and struggle to capture the complex nonlinear spatiotemporal dependencies embedded in the data. This paper introduces a novel latent factorization model, namely <underline>Auto-encoding <underline>Neural <underline>Tucker <underline>Factorization (ANTucF) for accurate spatiotemporal representation learning on the HDI tensor. It constructs a low-rank Tucker factorization-based neural network to capture a potential latent manifold in space and time, built upon three core ideas: a) applying density-oriented modeling principles with neural networks to facilitate latent feature learning via positional and temporal encoding of mode indices; b) constructing a Tucker interaction tensor to represent all possible spatiotemporal interactions among distinct spatial and temporal modes; and c) enhancing the uniqueness of the core tensor in Tucker factorization by incorporating nonlinear spatiotemporal representation learning via auto-encoding latent interaction learning. The ANTucF model outperforms several state-of-the-art LFT models in estimating missing observations on real-world datasets. Additionally, visualizations demonstrate its ability to capture finer spatiotemporal dynamics by nonlinearly exploiting an optimal Tucker core tensor using a data-driven approach.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"5795-5807"},"PeriodicalIF":10.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast Density Peaks Clustering Algorithm Based on Approximate k-Nearest Neighbors 基于近似k近邻的快速密度峰聚类算法

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-16 DOI: 10.1109/TKDE.2025.3589794

Shifei Ding;Chao Li;Xiao Xu;Lili Guo;Ling Ding;Xindong Wu

{"title":"Fast Density Peaks Clustering Algorithm Based on Approximate k-Nearest Neighbors","authors":"Shifei Ding;Chao Li;Xiao Xu;Lili Guo;Ling Ding;Xindong Wu","doi":"10.1109/TKDE.2025.3589794","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3589794","url":null,"abstract":"Density peaks clustering (DPC) is one of the density-based clustering algorithms and has been widely studied and applied in recent years because of its unique parameter, non-iteration and good robustness. However, it cannot effectively identify the cluster centers, and time and space complexities are too high. To this end, this paper proposes a fast density peaks clustering algorithm based on approximate <italic>k-nearest neighbors (FDPAN). Firstly, it uses Balanced K-means based Hierarchical K-means (BKHK) method to partition the data and quickly find the approximate <italic>k-nearest neighbors (AKNN), improving the algorithm’s efficiency on large-scale high-dimensional data. Meanwhile, three-way clustering is used to improve the neighbor search of the boundary points of the partition. Then, the local density and relative distance of DPC are recalculated by AKNN. Finally, according to the similar density chain, the connected high-density points are labeled while searching for the cluster center, and the remaining points are assigned to the clusters where their nearest higher-density points are located. Theoretical analysis and experiments on synthetic and real datasets show that FDPAN can obtain higher clustering results and shorten the operation time on large-scale high-dimensional data compared with DPC and its variants.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"5878-5889"},"PeriodicalIF":10.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GAN-Based Hybrid Sampling Method for Transaction Fraud Detection 基于gan的交易欺诈检测混合抽样方法

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-16 DOI: 10.1109/TKDE.2025.3589885

Yu Xie;Junkai Shan;Lifei Wei;Jiamin Yao;MengChu Zhou

{"title":"GAN-Based Hybrid Sampling Method for Transaction Fraud Detection","authors":"Yu Xie;Junkai Shan;Lifei Wei;Jiamin Yao;MengChu Zhou","doi":"10.1109/TKDE.2025.3589885","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3589885","url":null,"abstract":"In the digital era, effective Transaction Fraud Detection (TFD) is essential to ensuring financial security. The considerable class imbalance, with legitimate transactions vastly outnumbering fraudulent ones, presents a significant challenge for TFD models to accurately identify fraudulent patterns. While existing sample-balancing strategies address class imbalance effectively in many contexts, they often fall short in TFD due to fraudsters’ sophisticated concealment tactics, which lead to pronounced behavioral overlap between fraudulent and legitimate transactions. In this paper, we introduce a novel Generative Adversarial Network-based Hybrid Sampling method (GANHS) to effectively address the class imbalance issue. GANHS employs a dual-discriminator generative adversarial network to generate synthetic samples that accurately reflect the characteristics of fraudulent activity, while an adaptive neighborhood-based undersampling technique refines these samples to minimize overlap with legitimate ones. This hybrid approach not only enhances the model’s ability to learn fraud patterns by generating high-quality samples but also improves its resilience against highly concealed fraudulent activities. Experiments on real-world and public datasets demonstrate that GANHS outperforms its competitive peers, with gains of 0.5%–8.7% in average <inline-formula><tex-math>$F_{1}$</tex-math></inline-formula>-Score and 1.0%–7.0% in G-mean, highlighting its strong potential for improving the reliability and effectiveness of TFD systems in complex, high-risk financial scenarios.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"5905-5918"},"PeriodicalIF":10.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference 教mlp掌握异构图结构知识，以实现高效、准确的推理

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-16 DOI: 10.1109/TKDE.2025.3589596

Yunhui Liu;Xinyi Gao;Tieke He;Jianhua Zhao;Hongzhi Yin

{"title":"Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference","authors":"Yunhui Liu;Xinyi Gao;Tieke He;Jianhua Zhao;Hongzhi Yin","doi":"10.1109/TKDE.2025.3589596","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3589596","url":null,"abstract":"Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in various heterogeneous graph learning tasks, owing to their superiority in capturing the intricate relationships and diverse relational semantics inherent in heterogeneous graph structures. However, the neighborhood-fetching latency incurred by structure dependency in HGNNs makes it challenging to deploy for latency-constrained applications that require fast inference. Inspired by recent GNN-to-MLP knowledge distillation frameworks, we introduce HG2M and HG2M+ to combine both HGNN’s superior performance and MLP’s efficient inference. HG2M directly trains student MLPs with node features as input and soft labels from teacher HGNNs as targets, and HG2M+ further distills reliable and heterogeneous semantic knowledge into student MLPs through reliable node distillation and reliable meta-path distillation. Experiments conducted on six heterogeneous graph datasets show that despite lacking structural dependencies, HG2Ms can still achieve competitive or even better performance than HGNNs and significantly outperform vanilla MLPs. Moreover, HG2Ms demonstrate a 379.24× speedup in inference over HGNNs on the large-scale IGB-3M-19 dataset, showcasing their ability for latency-sensitive deployments.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6189-6201"},"PeriodicalIF":10.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0