Information Fusion最新文献_第7页

Data augmentation with attentional feature aggregation for node classification in GNNs 基于关注特征聚合的gnn节点分类数据增强

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-06-01 Epub Date: 2025-12-24 DOI: 10.1016/j.inffus.2025.104089

Guangquan Lu , Shilong Lin , Yuxuan Hu , Debo Cheng , Chen Li , Shichao Zhang

Graph Neural Networks (GNNs) have demonstrated remarkable success in classification tasks on graphs, including multimedia applications such as image recognition, video analysis, and recommendation systems. However, most GNNs methods assume that the category of samples is balanced, which contradicts real-world class distribution. In practice, imbalanced category distribution often causes GNNs to neglect minority-class nodes during training, which in turn negatively impacts overall classification performance. Existing methods still face key challenges, including insufficient feature learning and inadequate generation of node homogeneity. To tackle these challenges, we propose GraphAFA, a novel Graph-based method that utilizes Attentional Feature Aggregation to generate a small number of synthetic class nodes, thereby promoting sample equilibrium. GraphAFA consists of two key components: attention-based feature extraction and neighbor-aware node aggregation. Firstly, GraphAFA constructs a feature space and utilizes an attention mechanism to extract node features, enabling effective learning higher-order relationships among nodes. Secondly, during the node generation process, GraphAFA aggregates information from neighboring nodes to capture shared features, ensuring the newly generated nodes are more homogeneous and reducing the risk of generating heterogeneous samples. Finally, GraphAFA connects edges to the newly generated nodes, integrating them into the graph for downstream classification. Comprehensive experiments on three benchmark datasets show that GraphAFA consistently outperforms state-of-the-art methods in class-imbalanced node classification.

图神经网络（gnn）在图的分类任务中取得了显著的成功，包括图像识别、视频分析和推荐系统等多媒体应用。然而，大多数gnn方法假设样本的类别是平衡的，这与现实世界的类别分布相矛盾。在实践中，不平衡的类别分布往往会导致gnn在训练过程中忽略少数类节点，从而对整体分类性能产生负面影响。现有方法仍然面临着特征学习不足和节点同质性生成不足等关键挑战。为了解决这些挑战，我们提出了一种新的基于图的方法graphhafa，该方法利用注意力特征聚合来生成少量合成类节点，从而促进样本平衡。graphhafa包括两个关键组件：基于注意力的特征提取和邻居感知的节点聚合。首先，GraphAFA构建特征空间，利用注意机制提取节点特征，实现节点间高阶关系的有效学习。其次，在节点生成过程中，graphhafa对相邻节点的信息进行聚合，捕获共享特征，保证新生成的节点更加同质，降低生成异构样本的风险。最后，GraphAFA将边连接到新生成的节点，将它们整合到图中进行下游分类。在三个基准数据集上的综合实验表明，graphhafa在类不平衡节点分类方面始终优于最先进的方法。

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引用次数: 0

ChatAssistDesign: A language-interactive framework for iterative vector floorplan generation via conditional diffusion ChatAssistDesign：一种通过条件扩散生成迭代矢量平面图的语言交互框架

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-06-01 Epub Date: 2025-12-20 DOI: 10.1016/j.inffus.2025.104091

Luping Li , Xing Su , Han Lin , Haoying Han , Chao Fan , Zhao Zhang , Hongzhe Yue

Architectural design, a complex optimization process requiring iterative revisions by skilled architects, increasingly leverages computational tools. While deep generative models show promise in automating floorplan generation, two key limitations persist: (1) reliance on domain expertise, creating high technical barriers for non-experts, and (2) lack of iterative refinement capabilities, limiting post-generation adjustments. To address these challenges, we propose ChatAssistDesign, an interactive text-driven framework combining (1) Floorplan Designer, a large language model (LLM) agent guiding users through design workflows, and (2) ConDiffPlan, a vector-based conditional diffusion model for layout generation. Extensive experimental results demonstrate that our framework achieves significant improvements over state-of-the-art methods in terms of layout diversity, visual realism, text-to-layout alignment accuracy, and crucially, the ability to support iterative refinement while maintaining high robustness against constraint conflicts. By abstracting design complexity from user skill and enabling dynamic post hoc edits, our approach reduces entry barriers and improves integration with downstream tasks.

架构设计是一个复杂的优化过程，需要熟练的架构师进行迭代修改，它越来越多地利用计算工具。虽然深度生成模型在自动化平面图生成方面显示出前景，但仍然存在两个关键限制：(1)依赖领域专业知识，为非专家创造了很高的技术壁垒；(2)缺乏迭代细化能力，限制了生成后的调整。为了应对这些挑战，我们提出了ChatAssistDesign，这是一个交互式文本驱动框架，它结合了(1)Floorplan Designer，一个引导用户完成设计工作流的大型语言模型（LLM）代理，以及(2)ConDiffPlan，一个用于布局生成的基于向量的条件扩散模型。广泛的实验结果表明，我们的框架在布局多样性、视觉真实感、文本到布局的对齐精度方面比最先进的方法取得了显著的改进，最重要的是，支持迭代改进的能力，同时保持对约束冲突的高鲁棒性。通过从用户技能中抽象出设计复杂性，并启用动态的事后编辑，我们的方法减少了入门障碍，并提高了与下游任务的集成。

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引用次数: 0

Federated learning in oncology: Bridging artificial intelligence innovation and privacy protection 肿瘤学中的联合学习：连接人工智能创新和隐私保护

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-06-01 Epub Date: 2026-01-16 DOI: 10.1016/j.inffus.2026.104154

Xin Qi , Tao Xu , Chengrun Dang , Zhuang Qi , Lei Meng , Han Yu

Artificial intelligence (AI), including machine learning and deep learning models, is increasingly transforming oncology by providing powerful tools to analyze complex multidimensional data. However, developing reliable and generalizable models requires large-scale training datasets, which are often constrained by privacy regulations and the decentralized nature of medical data across institutions. Federated learning has recently emerged as a promising approach that enables collaborative model training across multiple sites without sharing raw data. This survey presents the fundamental principles and architectural frameworks of federated learning, highlighting its strengths in protecting data privacy, improving model robustness, and facilitating the integration of multi-omics and multi-modal datasets. Key applications in cancer detection, prognosis prediction, and treatment response prediction are discussed, underscoring its potential to support clinical decision-making. Moreover, the survey highlights major challenges in applying federated learning to oncology and outlines key directions to advance precision medicine, including the integration of multi-modal data, foundation models, causal reasoning, and continual learning. With ongoing technological advancements, federated learning holds great promise to bridge AI innovation and privacy protection in oncology.

人工智能（AI），包括机器学习和深度学习模型，通过提供强大的工具来分析复杂的多维数据，正在日益改变肿瘤学。然而，开发可靠和可推广的模型需要大规模的训练数据集，这通常受到隐私法规和医疗数据跨机构分散性质的限制。联邦学习最近成为一种很有前途的方法，它可以在不共享原始数据的情况下跨多个站点进行协作模型训练。本研究介绍了联邦学习的基本原理和架构框架，强调了其在保护数据隐私、提高模型鲁棒性以及促进多组学和多模态数据集集成方面的优势。讨论了其在癌症检测、预后预测和治疗反应预测中的关键应用，强调了其支持临床决策的潜力。此外，该调查还强调了将联合学习应用于肿瘤学的主要挑战，并概述了推进精准医学的关键方向，包括多模式数据的集成、基础模型、因果推理和持续学习。随着技术的不断进步，联合学习在肿瘤学领域的人工智能创新和隐私保护方面有着巨大的前景。

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引用次数: 0

Shrinkage matters: evidence from accuracy-diversity trade-off in regression ensembles 收缩问题：来自回归集合中准确性-多样性权衡的证据

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-06-01 Epub Date: 2025-12-19 DOI: 10.1016/j.inffus.2025.104073

Han Feng , Pengyang Song , Yinuo Ren , Hanfeng Zhou , Jue Wang

Regression ensembles, a competitive machine learning technique, have gained popularity in recent years. Popular ensemble schemes have evolved from equal weights (EWs), which utilize simple averages, to optimal weights (OWs), which optimize weights by minimizing mean squared error (MSE). Extensive research has not only validated the robustness of EWs but also introduced the concept of shrinkage, shrinking OWs towards EWs. This paper tackles the ensemble challenge through diversity theory, where ensemble MSE is decomposed into two components: global error and global diversity. Within the decomposition framework, OWs typically minimize global error at the expense of reduced global diversity, while EWs tend to maximize global diversity but often ignore the accuracy. To address the accuracy-diversity trade-off, we derive an optimal shrinkage factor that manages to minimize the ensemble MSE. Simulation results reveal the mediation effect of shrinkage weights, and empirical experiments on six UCI datasets and Brent monthly future prices demonstrate the superiority of the proposed method, whose mechanism is further expounded through an in-depth analysis of the shrinkage components. Overall, our approach provides a novel perspective on the efficacy of shrinkage in regression ensembles.

回归集成是一种有竞争力的机器学习技术，近年来越来越受欢迎。流行的集成方案已经从利用简单平均值的等权（EWs）发展到通过最小化均方误差（MSE）来优化权重的最优权（OWs）。大量的研究不仅验证了EWs的鲁棒性，而且引入了收缩的概念，将OWs收缩到EWs。本文通过多样性理论解决了集成的挑战，将集成MSE分解为两个部分：全局误差和全局多样性。在分解框架中，OWs通常以降低全局多样性为代价来最小化全局误差，而EWs倾向于最大化全局多样性，但往往忽略了准确性。为了解决准确性和多样性之间的权衡，我们推导了一个最佳收缩因子，以最小化集合MSE。模拟结果揭示了收缩率权重的中介作用，在6个UCI数据集和布伦特月度期货价格上的实证实验证明了该方法的优越性，并通过对收缩率分量的深入分析，进一步阐述了该方法的作用机理。总的来说，我们的方法提供了一个新颖的视角对收缩的有效性在回归集合。

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引用次数: 0

A comprehensive benchmark of spatial encoding methods for tabular data with deep neural networks 基于深度神经网络的表格数据空间编码方法的综合评测

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-06-01 Epub Date: 2025-12-25 DOI: 10.1016/j.inffus.2025.104088

Jiayun Liu , Manuel Castillo-Cara , Raúl García-Castro

Despite the success of deep neural networks on perceptual data, their performance on tabular data remains limited, where traditional models still outperform them. A promising alternative is to transform tabular data into synthetic images, enabling the use of vision architectures such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). However, the literature lacks a large-scale, standardized benchmark evaluating these transformation techniques. This work presents the first comprehensive evaluation of nine spatial encoding methods across 24 diverse regression and classification datasets. We assess performance, scalability, and computational trade-offs under a unified framework with rigorous hyperparameter optimization. Our results reveal a performance landscape structured by data regimes, defined by sample size (N) and dimensionality (d), and show that the transformation method exerts a significantly stronger influence on predictive performance than the chosen vision architecture. In particular, REFINED emerges as the most robust transformation across tasks and datasets. Hybrid models (CNN+MLP, ViT+MLP) consistently reduce predictive variance, offering advantages especially in smaller datasets, yet play a secondary role. These findings suggest that transforming tabular data into synthetic images is a powerful, yet data-dependent, strategy. This benchmark provides clear guidance for researchers and practitioners, offering key insights into scalability, transformation behavior, and architectural interplay, establishing a comprehensive reference for future research on spatial encodings for tabular data.

尽管深度神经网络在感知数据上取得了成功，但它们在表格数据上的表现仍然有限，传统模型仍然优于它们。一个有希望的替代方案是将表格数据转换为合成图像，从而使用卷积神经网络（cnn）和视觉变压器（ViTs）等视觉架构。然而，文献缺乏一个大规模的，标准化的基准评估这些转换技术。这项工作提出了跨24个不同的回归和分类数据集的九种空间编码方法的第一个综合评价。我们在具有严格超参数优化的统一框架下评估性能、可伸缩性和计算权衡。我们的研究结果揭示了由数据体系构成的性能景观，由样本量(N)和维数(d)定义，并表明转换方法对预测性能的影响明显强于所选的视觉架构。特别是，refine是跨任务和数据集的最健壮的转换。混合模型（CNN+MLP, ViT+MLP）持续降低预测方差，特别是在较小的数据集上提供优势，但起次要作用。这些发现表明，将表格数据转换为合成图像是一种强大但依赖于数据的策略。该基准为研究人员和实践者提供了明确的指导，提供了对可伸缩性、转换行为和体系结构相互作用的关键见解，为表格数据的空间编码的未来研究建立了全面的参考。

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引用次数: 0

Uncertainty-aware multi-view evidence fusion for feature selection in brain network analysis 脑网络分析中特征选择的不确定性感知多视角证据融合

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-06-01 Epub Date: 2025-12-27 DOI: 10.1016/j.inffus.2025.104083

Yuepeng Chen , Weiping Ding , Shangce Gao , Jiaru Yang , Tianyi Zhou , Qichong Hua , Fan Fu

Accurate diagnosis of schizophrenia remains challenging due to the lack of reliable biomarkers. Dynamic functional connectivity (dFC) derived from resting-state fMRI provides a powerful representation of temporal brain dynamics; however, its inherently multi-view structure introduces severe challenges, including high dimensionality, heterogeneity across views, and uncertainty caused by preprocessing. Such uncertainty directly affects feature selection because unreliable features may degrade both diagnostic accuracy and interpretability. However, most existing feature selection methods fail to explicitly model and utilize uncertainty. To address these challenges, we propose a multi-view feature selection method based on evidence theory that explicitly models uncertainty while capturing inter-view consistency and complementarity. This approach enables the selection of both shared and view-specific discriminative patterns that are often overlooked in dynamic brain network analysis. We further introduce an information-theoretic consistency constraint to extract reliable shared information and an uncertainty-weighted loss based on the Dirichlet distribution to prioritize complementary features with lower uncertainty. By integrating confidence measures across views through evidence fusion, our method effectively quantifies and leverages uncertainty to optimize feature selection. Extensive experiments on three independent rs-fMRI schizophrenia datasets demonstrate improved classification accuracy and robustness, providing an interpretable and reliable tool for identifying biomarkers in neuropsychiatric research.

由于缺乏可靠的生物标志物，精神分裂症的准确诊断仍然具有挑战性。动态功能连接（dFC）来源于静息状态的功能磁共振成像提供了一个强有力的大脑动态表征；然而，其固有的多视图结构带来了严峻的挑战，包括高维性、跨视图的异构性以及预处理带来的不确定性。这种不确定性直接影响特征选择，因为不可靠的特征可能会降低诊断的准确性和可解释性。然而，大多数现有的特征选择方法都没有明确地建模和利用不确定性。为了解决这些挑战，我们提出了一种基于证据理论的多视图特征选择方法，该方法在捕获视图间一致性和互补性的同时明确地建模不确定性。这种方法可以选择共享和特定视图的判别模式，这些模式在动态大脑网络分析中经常被忽视。我们进一步引入了信息论的一致性约束来提取可靠的共享信息，并引入了基于Dirichlet分布的不确定性加权损失来优先考虑不确定性较低的互补特征。通过证据融合整合不同视图的置信度度量，该方法有效地量化和利用不确定性来优化特征选择。在三个独立的rs-fMRI精神分裂症数据集上进行的大量实验表明，分类准确性和稳健性得到了提高，为神经精神病学研究中识别生物标志物提供了一种可解释和可靠的工具。

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引用次数: 0

Bridging the sim-to-real gap in RF localization with large-scale synthetic pretraining 用大规模合成预训练弥合射频定位的模拟与真实差距

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-06-01 Epub Date: 2025-12-30 DOI: 10.1016/j.inffus.2025.104104

Armen Manukyan , Rafayel Mkrtchyan , Ararat Saribekyan , Theofanis P. Raptis , Hrant Khachatrian

Radio frequency (RF) fingerprinting is a promising localization technique for GPS-denied environments, yet it tends to suffer from a fundamental limitation: Poor generalization to previously unmapped areas. Traditional methods such as k-nearest neighbors (k-NN) perform well where data is available but may fail on unseen streets, limiting real-world deployment. Deep learning (DL) offers potential remedies by learning spatial-RF patterns that generalize, but requires far more training data than what simple real-world measurement campaigns can provide. In this paper, we investigate whether synthetic data can bridge this generalization gap. Using (i) a real-world dataset from Rome and (ii) NVIDIA’s open-source ray-tracing simulator Sionna, we generate synthetic datasets under varying realism and scale conditions. Specifically, we use Dataset A containing real-world measurements with real base stations (BS) and real signals, and create Dataset B using real BS locations but simulated signals, Dataset C with both simulated BS locations and signals, and Dataset B’ which represents an optimized version of Dataset B where BS parameters are calibrated via Gaussian Process to maximize signal correlation with Dataset A. Our evaluation reveals a pronounced sim-to-real gap: Models achieving 25m error on synthetic data degrade to 184m on real data. Nonetheless, pretraining on synthetic data reduces real-world localization error from 323m to 162m; a 50% improvement over real-only training. Notably, simulation fidelity proves more important than scale: A smaller calibrated dataset (53K samples) outperforms a larger uncalibrated one (274K samples). To further evaluate the generalization capabilities of the models, we conduct experiments on an unseen geographical region using a real-world dataset from Oslo. In the zero-shot setting, the models achieve a root mean square error (RMSE) of 132.2m on the entire dataset, and 61.5m on unseen streets after fine-tuning on Oslo data. While challenges remain before meeting more practical localization accuracy, this work provides a systematic study in the field of wireless communication of synthetic-to-real transfer in RF localization and highlights the value of simulation-aware pretraining for generalizing DL models to real-world scenarios.

射频（RF）指纹识别是一种很有前途的定位技术，适用于没有gps的环境，但它往往有一个基本的局限性：对以前未映射的区域的泛化能力差。传统的方法，如k近邻（k-NN）在数据可用的情况下表现良好，但在未知的街道上可能会失败，限制了现实世界的部署。深度学习（DL）通过学习泛化的空间射频模式提供了潜在的补救措施，但需要更多的训练数据，而不是简单的现实世界测量活动所能提供的。在本文中，我们研究了合成数据是否可以弥补这一泛化差距。使用(i)来自罗马的真实世界数据集和（ii） NVIDIA的开源光线追踪模拟器Sionna，我们在不同的现实主义和规模条件下生成合成数据集。具体来说，我们使用包含真实基站（BS）和真实信号的真实测量数据集A，并使用真实BS位置但模拟信号创建数据集B，使用模拟BS位置和信号创建数据集C，以及代表数据集B的优化版本的数据集B，其中BS参数通过高斯过程校准以最大化与数据集A的信号相关性。我们的评估揭示了明显的模拟与真实差距：在合成数据上达到25m误差的模型在真实数据上降低到184m。尽管如此，对合成数据的预训练将真实世界的定位误差从323m减少到162m；比真实训练提高了50%。值得注意的是，模拟保真度比规模更重要：较小的校准数据集（53K样本）优于较大的未校准数据集（274K样本）。为了进一步评估模型的泛化能力，我们使用来自奥斯陆的真实数据集在一个未知的地理区域进行了实验。在零射击设置下，模型在整个数据集上的均方根误差（RMSE）为132.2m，在对奥斯陆数据进行微调后，在看不见的街道上的均方根误差（RMSE）为61.5m。虽然在满足更实际的定位精度之前仍然存在挑战，但这项工作提供了射频定位中合成到真实传输的无线通信领域的系统研究，并强调了模拟感知预训练对将DL模型推广到现实场景的价值。

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引用次数: 0

Team collaboration-oriented multi-agent pathfinding and probabilistic verification 面向团队协作的多智能体寻路与概率验证

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-06-01 Epub Date: 2026-01-10 DOI: 10.1016/j.inffus.2026.104125

Xia Wang , Jun Liu , Chris D. Nugent , Shaobing Xu , Guanfeng Wu

Multi-agent pathfinding and its reliable execution in stochastic environments represent a critical challenge for real-world applications, demanding both the planning of efficient paths and the formal assurance of safe, conflict-free operation. This paper introduces a novel methodology framework to address this dual requirement. To maximize operational efficiency, we introduce a strategy for optimal goal allocation for team collaboration, integrating it with the conflict-based search algorithm to minimize the total move counts required for mission completion. The second component is an integrated verification process grounded in probabilistic model checking. We model the multi-agent path execution process under stochastic uncertainties using a Markov decision process. By leveraging the probabilistic model checker and probabilistic computation tree logic, the framework formally verifies critical safety properties, ensuring conflict-free and deadlock-free path execution. Furthermore, it evaluates the effectiveness of proposed behavioral constraints designed to mitigate stochastic delays, thereby verifying the overall system safety. By fusing multi-agent planning, probabilistic reasoning, and formal logic-based verification, the proposed framework establishes a foundation amenable to natural extension for addressing multi-agent decision-making and uncertainty estimation. Case study results demonstrate that our methodology effectively selects the pathfinding solution with the minimum move count while significantly enhancing overall system safety through these formally verified behavioral constraints.

多智能体寻路及其在随机环境中的可靠执行对现实世界的应用来说是一个关键的挑战，它既要求规划有效的路径，又要求正式保证安全、无冲突的操作。本文介绍了一种新的方法框架来解决这一双重要求。为了使操作效率最大化，我们引入了一种团队协作的最佳目标分配策略，将其与基于冲突的搜索算法相结合，以最小化完成任务所需的总移动次数。第二个组成部分是基于概率模型检查的集成验证过程。利用马尔可夫决策过程对随机不确定性下的多智能体路径执行过程进行建模。通过利用概率模型检查器和概率计算树逻辑，框架正式验证关键安全属性，确保无冲突和无死锁的路径执行。此外，它还评估了旨在减轻随机延迟的行为约束的有效性，从而验证了整个系统的安全性。通过融合多智能体规划、概率推理和基于形式逻辑的验证，该框架为解决多智能体决策和不确定性估计问题建立了一个可自然扩展的基础。案例研究结果表明，我们的方法有效地选择了移动次数最少的寻路解决方案，同时通过这些正式验证的行为约束显著提高了整体系统的安全性。

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引用次数: 0

Subgraph-focused biomedical knowledge embedding with bi-semantic encoder for multi-type drug-drug interaction prediction 基于双语义编码器的生物医学知识嵌入多类型药物-药物相互作用预测

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-06-01 Epub Date: 2025-12-31 DOI: 10.1016/j.inffus.2025.104109

Xiangpeng Bi , Wenjian Ma , Huasen Jiang , Qing Cai , Jie Nie , Zhiqiang Wei , Shugang Zhang

Identifying multi-type drug-drug interactions (DDIs) enables more precise assessment of drug safety risks and provides targeted guidance for combination therapy, making it a critical task in pharmacology. Given it can directly integrate diverse biomedical information and effectively model the intricate mechanisms underlying drug interaction, knowledge graph (KG)-based approaches have emerged for predicting DDIs. Recent advances have shown great promise in this regard; however, existing solutions still overlook three critical issues: 1) neglect of information sparsity, 2) neglect of polyadic interactions, and 3) lack of fusion paradigm, which severely hinder the comprehensive identification and understanding of drug interaction patterns. To address these issues, we introduce a Bi-Semantic encoDer-dRiven knowledge sUbGraph representation learning framework (Bi-SemDRUG) for multi-type DDI prediction. Bi-SemDRUG proposes a multi-view knowledge subgraph partitioning strategy to extract drug-related refined topological structures from large-scale knowledge graphs, thereby reducing the interference of irrelevant information. Furthermore, Bi-SemDRUG incorporates a bi-semantic subgraph encoder that effectively uncovers multi-order semantic relationships embedded within the knowledge subgraphs. Finally, we propose a general paradigm for information fusion to facilitate the integration of multi-level drug-related information. Exhaustive experiments on three benchmark datasets demonstrate that our proposed model achieves state-of-the-art performance compared to other baseline methods and exhibits good generalization in large-scale DDI prediction. Additionally, case studies emphasize its capacity to offer a more comprehensive insight into the underlying mechanisms of DDIs.

多类型药物相互作用（ddi）的识别可以更精确地评估药物安全风险，为联合治疗提供有针对性的指导，是药理学中的一项重要任务。基于知识图（knowledge graph， KG）的ddi预测方法可以直接整合多种生物医学信息，有效地模拟药物相互作用的复杂机制。最近的进展在这方面显示出很大的希望；然而，现有的解决方案仍然忽视了三个关键问题：1)忽视信息稀疏性；2)忽视多元相互作用；3)缺乏融合范式，严重阻碍了对药物相互作用模式的全面识别和理解。为了解决这些问题，我们引入了一个用于多类型DDI预测的双语义编码器驱动的知识子图表示学习框架（Bi-SemDRUG）。Bi-SemDRUG提出了一种多视图知识子图划分策略，从大规模知识图中提取与药物相关的精细拓扑结构，从而减少不相关信息的干扰。此外，Bi-SemDRUG集成了一个双语义子图编码器，可以有效地揭示嵌入在知识子图中的多阶语义关系。最后，我们提出了一种通用的信息融合范式，以促进多层次药物相关信息的整合。在三个基准数据集上的详尽实验表明，与其他基准方法相比，我们提出的模型达到了最先进的性能，并且在大规模DDI预测中表现出良好的泛化。此外，案例研究强调其提供对ddi潜在机制的更全面洞察的能力。

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引用次数: 0

FedEGL: Edge-assisted federated graph learning FedEGL：边缘辅助联邦图学习

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-06-01 Epub Date: 2026-01-02 DOI: 10.1016/j.inffus.2025.104118

Haitao Wang , Aojie Luo , Wenchao Xu , Haozhao Wang , Yichen Li , Yining Qi , Rui Zhang , Ruixuan Li

Federated graph learning excels in learning graph-structured data that are distributed across multiple clients. However, the partition of graph data results in each client only possessing a subgraph, lacking its neighbor nodes, which significantly degrades accuracy. Although exchanging original nodes can address this issue, it requires interaction with a remote server, not only causing significant communication delays but also leaking data privacy. To tackle this, this paper proposes an edge-server-assisted federated graph learning approach, namely FedEGL, which aggregates and exchanges intermediate features of approximated nodes through a third-party edge server, performing cross-client feature alignment and dynamic weighted aggregation while dynamically allocating privacy budgets with adaptive differential privacy to preserve node privacy. Additionally, differential privacy is introduced to protect the privacy of approximated node features by dynamically allocating privacy budgets. Experimental results show that our method achieves accuracy close to that in centralized settings, with the classification accuracy improved by up to 8% compared to the latest baseline. This method can improve model accuracy while protecting privacy, providing an effective solution to the subgraph partitioning problem in federated graph learning.

联邦图学习擅长学习分布在多个客户机上的图结构数据。然而，图数据的分区导致每个客户端只拥有一个子图，而缺乏它的邻居节点，这大大降低了精度。虽然交换原始节点可以解决这个问题，但它需要与远程服务器进行交互，这不仅会导致严重的通信延迟，还会泄露数据隐私。为了解决这个问题，本文提出了一种边缘服务器辅助的联邦图学习方法，即FedEGL，该方法通过第三方边缘服务器聚合和交换近似节点的中间特征，进行跨客户端特征对齐和动态加权聚合，同时使用自适应差分隐私动态分配隐私预算，以保护节点隐私。此外，引入差分隐私，通过动态分配隐私预算来保护近似节点特征的隐私。实验结果表明，该方法的准确率接近集中式设置，与最新基线相比，分类准确率提高了8%。该方法在保护隐私的同时提高了模型的准确性，为联邦图学习中的子图划分问题提供了一种有效的解决方案。

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