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graph convolutional networks-based method for uncertainty quantification of building design loads

Building Simulation 2025年第2期18卷 321-337页

作者： Jie Lu Zeyu Zheng Chaobo Zhang Yang Zhao Chenxin Feng Ruchi Choudhary Institute of Refrigeration and Cryogenics Zhejiang UniversityHangzhouChina Energy Efficient Cities Initiative Department of EngineeringUniversity of CambridgeCambridgeUK Department of Energy Engineering Zhejiang UniversityHangzhouChina Department of the Built Environment Eindhoven University of TechnologyEindhoventhe Netherlands Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province Jiaxing Research InstituteZhejiang UniversityJiaxingChina Data-centric Engineering The Alan Turing InstituteLondonUK

Uncertainty quantification of building design loads is essential to efficient and reliable building energy planning in the design *** data-driven methods struggle to generalize across buildings with diverse shapes due to limitations in representing complex geometric *** tackle this issue,a graph convolutional networks(GCN)-based uncertainty quantification method is *** graph-based approach is introduced to represent building shapes by dividing them into blocks and defining their spatial relationships through nodes and *** method effectively captures complex building characteristics,enhancing the generalization *** approach leveraging GCN could estimate design loads by understanding the impact of diverse uncertain ***,a class activation map is formulated to identify key uncertain factors,guiding the selection of important design parameters during the building design *** effectiveness of this method is evaluated through comparison with four widely-used data-driven *** indicate that the mean absolute percentage errors(MAPE)for statistical indicators of uncertainty quantification are under 6.0%and 4.0%for cooling loads and heating loads,*** proposed method is demonstrated to quantify uncertainty in building design loads with outstanding generalization *** regard to time costs,the computation time of the proposed method is reduced from 331 hours to 30 seconds for a twenty-floor building compared to a conventional physics-based method.

关键词： building design loads uncertainty quantification data-driven model graph convolutional networks Monte Carlo simulation

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graph convolutional networks With Collaborative Feature Fusion for Sequential Recommendation

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IEEE TRANSACTIONS ON BIG DATA 2025年第2期11卷 735-747页

作者： Gou, Jianping Cheng, Youhui Zhan, Yibing Yu, Baosheng Ou, Weihua Zhang, Yi Southwest Univ Coll Comp & Informat Sci Coll Software Chongqing 400715 Peoples R China Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Peoples R China JD Explore Acad Beijing 101111 Peoples R China Univ Sydney Sch Comp Sci Darlington NSW 2008 Australia Guizhou Normal Univ Sch Big Data & Comp Sci Guiyang 550003 Peoples R China Sichuan Univ Sch Comp Sci Chengdu Peoples R China

Sequential recommendation seeks to understand user preferences based on their past actions and predict future interactions with items. Recently, several techniques for sequential recommendation have emerged, primarily leveraging graph convolutional networks (GCNs) for their ability to model relationships effectively. However, real-world scenarios often involve sparse interactions, where early and recent short-term preferences play distinct roles in the recommendation process. Consequently, vanilla GCNs struggle to effectively capture the explicit correlations between these early and recent short-term preferences. To address these challenges, we introduce a novel approach termed graph convolutional networks with Collaborative Feature Fusion (COFF). Specifically, our method addresses the issue by initially dividing each user interaction sequence into two segments. We then construct two separate graphs for these segments, aiming to capture the user's early and recent short-term preferences independently. To obtain robust prediction, we employ multiple GCNs in a collaborative distillation manner, incorporating a feature fusion module to establish connections between the early and recent short-term preferences. This approach enables a more precise representation of user preferences. Experimental evaluations conducted on five popular sequential recommendation datasets demonstrate that our COFF model outperforms recent state-of-the-art methods in terms of recommendation accuracy.

关键词： Federated learning Feature extraction Big Data Collaboration graph convolutional networks Computer science Accuracy Feature fusion graph convolutional networks online distillation sequential recommendation

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Leveraging graph convolutional networks for Semi-supervised Learning in Multi-view Non-graph Data

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COGNITIVE COMPUTATION 2025年第2期17卷 1-15页

作者： Dornaika, F. Bi, J. Charafeddine, J. Univ Basque Country UPV EHU San Sebastian Spain Basque Fdn Sci Ikerbasque Bilbao Spain De Vinci Res Ctr De Vinci Higher Educ Paris France

Semi-supervised learning with a graph-based approach has gained prominence in machine learning, particularly in scenarios where labeling data involves substantial costs. graph convolution networks (GCNs) have found widespread application in semi-supervised learning, predominantly on graph-structured data such as citation and social networks. However, a noticeable gap exists in the application of these methods to non-graph multi-view data, such as collections of images. In an effort to address this gap, we introduce two innovative deep semi-supervised multi-view classification models specifically tailored for non-graph data. Both models share a common architecture, leveraging GCNs and integrating a label smoothing constraint. The primary distinction lies in the construction of the consensus similarity graph. The first model directly reconstructs the consensus graph from different views using a specialized objective function designed for flexible graph-based semi-supervised classification. In contrast, the second model independently reconstructs individual graphs and subsequently adaptively merges them into a unified consensus graph. Our experiments encompass various multiple-view image datasets. The results consistently demonstrate the superior performance of our proposed approach compared to traditional fusion methods with GCNs. In this research, we present two approaches for tackling semi-supervised classification challenges involving multiple views. One method is named Semi-supervised Classification with a Unified graph (SCUG), and the other is referred to as Semi-supervised Classification with a Fused graph (SC-Fused). Both methods share a common semi-supervised classification process, utilizing the GCN framework and incorporating label smoothing. However, the key distinction lies in the construction of the similarity graph. Unlike traditional ad hoc graph construction approaches, our proposed methods, SCUG and SC-Fused, estimate the unified graph or individual g

关键词： Multi-view data Semi-supervised learning graph estimation Consensus graph graph convolutional networks

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Community-Enhanced Dynamic graph convolutional networks for Rumor Detection on Social networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第2期12卷 818-831页

作者： Zhou, Wei Wang, Chenzhan Luo, Fengji Wang, Yu Gao, Min Wen, Junhao Chongqing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China Univ Sydney Sch Civil Engn Sydney NSW 2006 Australia

Along with the increasing popularization of social platforms, rumors in the Web environment have become one of the significant threats to human society. Existing rumor detection methods ignore modeling and analyzing the community structure of the rumor propagation network. This article proposes a new community-enhanced dynamic graph convolutional network (CDGCN) for effective rumor detection on online social networks, which utilize the communities formed in a rumor propagation process to improve rumor detection accuracy. CDGCN uses a designed method that combines node features and topology features to identify the communities and learn the community features of rumors. Following this, a graph convolutional network (GCN) with a community-aware attention mechanism is proposed to enable the nodes to dynamically aggregate information from their neighboring nodes' global and community features, effectively prioritizing critical neighborhood information, enhancing the representation of both local community structures and global network patterns for improved analytical performance. The final rumor representations generated by the GCN are processed by a classifier to detect false rumors. Comprehensive experiments and comparison studies are conducted on four real-world datasets to validate the effectiveness of CDGCN.

关键词： Feature extraction Social networking (online) Image edge detection Accuracy Recurrent neural networks Long short term memory graph convolutional networks Fake news Vectors Kernel Attention mechanism community detection graph convolutional network (GCN) rumor detection

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TMC-GCN: Encrypted Traffic Mapping Classification Method Based on graph convolutional networks

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Computers, Materials & Continua 2025年第2期82卷 3179-3201页

作者： Baoquan Liu Xi Chen Qingjun Yuan Degang Li Chunxiang Gu School of Cyberspace Security Zhengzhou UniversityZhengzhou450002China School of Cyberspace Security Information Engineering UniversityZhengzhou450001China Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001China

With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and graphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.

关键词： Encrypted traffic classification deep learning graph neural networks multi-layer perceptron graph convolutional networks

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Deep random walk inspired multi-view graph convolutional networks for semi-supervised classification

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APPLIED INTELLIGENCE 2025年第6期55卷 1-14页

作者： Chen, Zexi Chen, Weibin Yao, Jie Li, Jinbo Wang, Shiping Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Fujian Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent Inf Fuzhou 350116 Fujian Peoples R China China Unicom Res Inst Beijing 100176 Peoples R China

Recent studies highlight the growing appeal of multi-view learning due to its enhanced generalization. Semi-supervised classification, using few labeled samples to classify the unlabeled majority, is gaining popularity for its time and cost efficiency, particularly with high-dimensional and large-scale multi-view data. Existing graph-based methods for multi-view semi-supervised classification still have potential for improvement in further enhancing classification accuracy. Since deep random walk has demonstrated promising performance across diverse fields and shows potential for semi-supervised classification. This paper proposes a deep random walk inspired multi-view graph convolutional network model for semi-supervised classification tasks that builds signal propagation between connected vertices of the graph based on transfer probabilities. The learned representation matrices from different views are fused by an aggregator to learn appropriate weights, which are then normalized for label prediction. The proposed method partially reduces overfitting, and comprehensive experiments show it delivers impressive performance compared to other state-of-the-art algorithms, with classification accuracy improving by more than 5% on certain test datasets.

关键词： Multi-view learning Semi-supervised classification graph convolutional networks Deep random walk

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An indicator-based multi-objective evolutionary algorithm assisted by improved graph convolutional networks

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Yan, Pengguo Tian, Ye Liu, Yu Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Recently, graph convolutional networks (GCN) have attracted significant attention due to their superior performance in handling non-Euclidean spaces, which enables GCN to model and analyze complex data structures that cannot be handled by traditional methods. Neural network-based multi-objective evolutionary algorithms (NNMOEAs) have made significant strides, predominantly focusing on mapping the decision space to the objective space, but may fail to focus on the interconnectedness of solutions within the decision space. To address this problem, this paper proposes a two-stage multi-objective optimization algorithm that utilizes graph convolutional networks to enhance population evolution. In the initial stage, the algorithm employs cosine similarity to represent the population as graph-structured data. A hypervolume-guided self-attention update mechanism is then introduced to balance exploration and exploitation, achieved by establishing an exploratory neighborhood population alongside an expanded neighborhood population. In the subsequent stage, a key node detection strategy is implemented, which considers both the global influence and local mediation roles of nodes. This strategy selects individuals with highly concentrated information to generate new solutions, thereby facilitating a thorough exploration of the solution space. The proposed algorithm is evaluated against five state-of-the-art MOEAs across five benchmark test suites and five real-world problems. The experimental results demonstrate its superior performance in addressing robust, variable linkages and imbalance mapping multi-objective optimization problems, as well as its feasibility in practical problems.

关键词： graph convolutional networks Evolutionary algorithm Self-attention mechanism Key node detection strategy

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Determinate node selection for semi-supervised classification oriented graph convolutional networks

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2025年第1期25卷 1-10页

作者： Xiao, Yao Xu, Ji Yang, Jing Li, Shaobo Wang, Guoyin Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

graph convolutional networks (GCNs) have been proved successful in the field of semi-supervised node classification by extracting structural information from graph data. However, the random selection of labelled nodes used in GCNs may lead to unstable generalisation performance of GCNs. In this paper, we propose an efficient method for the deterministic selection of labelled nodes: the determinate node selection (DNS) algorithm. The DNS algorithm identifies two categories of representative nodes in the graph through structural analysis of the leading tree information granules: typical nodes and divergent nodes. These labelled nodes are selected by exploring the structure of the graph and determining the ability of the nodes to represent the distribution of data within the graph. The DNS algorithm can be applied quite simply on GCNs, and a wide range of semi-supervised graph neural network models for node classification tasks. Through extensive experimentation, we have demonstrated that the incorporation of the DNS algorithm leads to a remarkable improvement in the average accuracy of the model and a significant decrease in the standard deviation simultaneously, as compared to the vanilla method without a DNS module.

关键词： graph convolutional networks granular computing semi-supervised learning node classification

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Vehicular Speed Prediction Method for Highway Scenarios Based on Spatiotemporal graph convolutional networks and Potential Field Theory

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IEEE INTERNET OF THINGS JOURNAL 2025年第3期12卷 3330-3349页

作者： Li, Linheng An, Bocheng Zhang, Dapeng Gan, Rui Zhou, Zhi Qu, Xu Ran, Bin Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Southeast Univ Inst Internet Mobil Nanjing 211189 Peoples R China Southeast Univ Univ Wisconsin Madison Nanjing 211189 Peoples R China Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban T Nanjing 211189 Peoples R China Southwestern Univ Finance & Econ Sch Management Sci & Engn Chengdu 611100 Peoples R China

Traffic flow analysis largely depends on accurate predictions of microscopic speed. Due to the complexity and stochastic of real-world driving environments, traditional model-driven methods face significant challenges. In recent years, data-driven methods that combine advanced intelligent algorithms have emerged to address the issue of vehicle speed prediction. However, most existing studies primarily focus on the static spatiotemporal relationships between vehicles, with only a few exploring dynamic spatiotemporal correlations. In this study, a new graph structure, named the vehicle generalized-dynamic graph, is constructed to characterize the vehicle trajectory, emphasizing the spatial and temporal evolution of vehicles. In the spatial dimension, we employ the potential field theory to compute the field strength of vehicles in different motion states, enhancing the description of interdependencies among vehicles. In the temporal dimension, the movements of each vehicle at different time points are interrelated. Furthermore, we propose a novel neural network architecture, named the dynamic edge graph convolutional network (DEGCN), to address the vehicle speed prediction problem. The DEGCN model is evaluated through experiments on three real-world vehicle trajectory datasets. The experimental results demonstrate that the proposed model outperforms other baseline models in terms of prediction performance. Additionally, various ablation studies are conducted to evaluate the effectiveness of different components, the potential field theory, and the vehicle generalized-dynamic graph structure.

关键词： Predictive models Data models Microscopy Vehicle dynamics Dynamics Trajectory Accuracy Prediction algorithms graph convolutional networks Force graph convolutional network (GCN) potential field theory traffic flow vehicle generalized-dynamic graph vehicle speed prediction

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Rethinking information fusion: Achieving adaptive information throughput and interaction pattern in graph convolutional networks for collaborative filtering

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INFORMATION FUSION 2025年 120卷

作者： Wu, Jiaxin Pang, Chenglong Chen, Guangxiong Wan, Jihong Ouyang, Xiaocao Zhao, Jie Guangdong Univ Technol Sch Management Guangzhou Guangdong Peoples R China Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Guangdong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Southwestern Univ Finance & Econ Sch Comp & Artificial Intelligence Chengdu Peoples R China

graph convolutional networks (GCNs) are popular in collaborative filtering because they have robust information fusion mechanisms. However, existing GCN-based models generally treat all nodes uniformly within a bipartite graph, such as the classic e-commerce scenario with user nodes and item nodes, which overlooks variations in node types and node information amount. Furthermore, these models rarely consider the adaptability of different information fusion methods to various user interaction patterns across different scenarios. These oversights may result in the loss of node information and inadequate representation learning, thereby potentially leading to sub-optimal model performance and efficiency. To address these problems, we propose an adaptive GCN-based model, called AdaptGCN, which handles both node and layer heterogeneity. AdaptGCN introduces a new node information fusion method to achieve adaptive information throughput according to node type and node information amount. It also has different layer information fusion methods to handle various interaction patterns across scenarios. For the node information amount, we provide the corresponding definition and explore properties for quantifying such information. Based on the quantification properties, we design a unified quantification function and develop it into an adaptive quantification method according to the characteristics of different types of nodes, which we use for node information fusion in AdaptGCN to achieve adaptive information throughput for each node. Based on this, we further propose a unified layer information fusion method to address the varying interaction patterns across different scenarios. Extensive experimental results show that AdaptGCN outperforms state-of-the-art GCN-based models in terms of both performance and efficiency.

关键词： graph convolutional networks Collaborative filtering Recommendation system Information fusion

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