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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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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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Critical component analysis of cyber-physical power systems in cascading failures using graph convolutional networks: An energy-based approach

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SUSTAINABLE ENERGY GRIDS & networks 2025年 42卷

作者： Soleimani, Sajedeh Afshar, Ahmad Atrianfar, Hajar Amirkabir Univ Technol Dept Elect Engn 350 Hafez AveValiasr Sq Tehran *** Iran

Power systems, with increasing integration into communication networks, have evolved to become complex and interdependent cyber-physical power systems that are highly vulnerable to cascading failures. These failures, due to their propagation through the cyber and physical networks, often lead to severe disruptions. We employ improved percolation theory to model cascading failures triggered by malware cyber-attacks. Addressing the vulnerability of CPPS requires a comprehensive analysis that spans both the structural and functional dimensions of CPPS. This paper introduces a novel framework for vulnerability assessment in CPPS using graph convolutional networks (GCN). Our approach captures the topological complexities and dynamic characteristics of CPPS, incorporating the entropy of potential energy of power system as anew feature to predict and analyze failure propagation. Through Layer-wise Relevance Propagation (LRP), we subsequently quantify the influence of potential energy on system vulnerabilities. Critical components are identified by using LRP scores and an entropy weighting method (EWM). Simulation results based on the cyber-physical IEEE 39- bus and IEEE RTS-96 power systems as test cases, demonstrate the model's efficacy in identifying vulnerable nodes and branches and highlight the significant role of potential energy in cascading failures. This framework provides a comprehensive approach for real-time vulnerability assessments and resilience enhancement in CPPS.

关键词： Cascading failure Cyber-physical system Energy entropy Vulnerability metrics graph convolutional networks

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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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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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A real-time text recognition based on involution operators and graph convolutional networks

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2025年第2期22卷 1-13页

作者： Tong, Guoxiang Li, Yang Dong, Ming Peng, Dunlu Univ Shanghai Sci & Technol Coll Opt Elect & Comp Engn Shanghai 200093 Peoples R China

The ambiguity and stickiness of text in natural scenarios make text recognition models incorrectly recognize certain characters as others. To address the above problems and the computational scale of the model, we propose a real-time effective text recognition method (IGTR). The method introduces the involution operator in extracting feature sequences within a larger sensory field using a smaller number of parameters. Due to the dependency of the characters before and after the text line, after obtaining the text feature sequence, IGTR uses the gate recurrent unit (GRU) algorithm to obtain richer contextual semantic information and pairs it with the graph convolutional neural network (GCN) to further enhance the semantics of the text features on the basis of the structure of the graph. Finally, the CTC algorithm is used to optimize the computation process of the loss function, and the dynamic planning of the search path is adopted to further reduce the computation amount of the text recognition model. Comparative experiments on ICDAR 2013, ICDAR 2015 and SVT datasets show that the method can effectively recognize multi-scale fuzzy text and sticky text within 75ms. With no dictionary supervision, the Precog\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {P}_{\text {recog}}$$\end{document} reaches 94.2%, 89.7%, and 93.8%, respectively, which is competitive with other compared methods.

关键词： Natural scene text recognition Involution Gate recurrent unit graph convolutional networks Connectionist temporal classification

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Temporal Temperature Profile Prediction Using graph convolutional networks and Inverted Echosounder Measurements

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IEEE JOURNAL OF OCEANIC ENGINEERING 2025年第1期50卷 31-44页

作者： Zhang, Yu Cui, Xuerong Li, Juan Li, Lei Jiang, Bin Li, Shibao Liu, Jianhang China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Minist Nat Resources Technol Innovat Ctr Maritime Silk Rd Marine Resour Qingdao 266580 Peoples R China

Ocean temperature prediction is a prominent research topic in current ocean science. The empirical modal method, which is based on the inverse echosounder, is one of the most significant methods for analyzing the physical environment of the deep-sea sound layer. This method effectively inverts the temperature profile of the research area. In this article, we propose the time- and self-attention mechanism graph convolutional neural network (ASeTGCN) that uses the inverted data. Unlike traditional time-series forecasting methods, ASeTGCN utilizes graph convolutional networks to capture the inherent spatial correlation of the research area. It also employs self-attention mechanisms to address the nonuniformity of temperature profiles at varying depths. Lastly, it uses time-attention mechanisms to analyze the correlation of temperature profile sequences sampled at daily, weekly, and monthly frequencies. We conducted multiple comparative experiments and related ablation experiments on the proposed model, and our results indicate that the model can effectively extend the time series of temperature profiles in the research area with a root-mean-square error of only 0.19.

关键词： Ocean temperature Temperature measurement Convolution Sea measurements graph convolutional networks Time series analysis Temperature distribution Sea surface Echo sounders Data models Attention mechanism deep-sea tomography graph convolution network inverted echosounders (IES) ocean temperature profile prediction

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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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