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graph convolutional network for water network partitioning

EXPERT SYSTEMS WITH APPLICATIONS 2025年 261卷

作者： Chen, Yi-wen Hu, Si-qi Li, Ming Yan, Xue-song China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China Univ Calif Los Angeles Coll Letters & Sci Los Angeles CA 93106 USA Minist Educ Engn Res Ctr Nat Resource Informat Management & Di Wuhan 430074 Peoples R China

The partitioning of a water distribution network into several district-metered areas (DMAs) yields is of great benefits for the management of water distribution systems, including enhanced leak control, pollution monitoring, and pressure optimization. However, achieving an effective partitioning that simultaneously incorporates node characteristics and network connections is challenging. Previous studies have tackled water network partitioning by focusing on either node features or pipe connections individually. In response to the limitations of current approaches for water distribution network partitioning, this study introduces an innovative unsupervised clustering approach for node partitioning based on graph convolutional network (GCN) techniques. Initially, the network nodes are clustered using the Cross-Attention Fusion Based Enhanced graph convolutional network (CaEGCN) deep clustering algorithm, considering both the network's topological layout and hydraulic properties. The resulting clusters categorize the network nodes into distinct regions. We evaluate our approach and other methods through areal world water distribution network using five different metrics commonly used to evaluate applied in assessing water supply network zoning issues. By comparing and analyzing the performance of experiments with other schemes, the experimental results demonstrate that the CaEGCN deep clustering method featured a remarkable performance. The proposed method showcases its potential for practical application by offering a dependable and easily interpretable water network partitioning solution.

关键词： graph convolutional network Water distribution network Partition Clustering

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MVGNCDA: Identifying Potential circRNA-Disease Associations Based on Multi-view graph convolutional networks and network Embeddings

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2025年 1-14页

作者： Sun, Guicong Zheng, Mengxin Fan, Yongxian Pan, Xiaoyong Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Minist Educ ChinaDept Automat Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

Increasing evidences have indicated that circular RNAs play a crucial role in the onset and progression of various diseases. However, exploring potential disease-associated circRNAs using conventional experimental techniques remains both time-intensive and costly. Recently, various computational approaches have been developed to detect the circRNA-disease associations. Nevertheless, due to the sparsity of the data and the inefficient utilization of similarity representation, it is still a challenge to effectively detect unknown circRNA-disease associations using multisource data. In this work, we propose an innovative computational framework, MVGNCDA, which merges a multi-view graph convolutional network (GCN) and biased random walk-based network embeddings to evaluate potential circRNA-disease associations from multisource data. First, we calculate disease semantic similarity, circRNA functional similarity, and their Gaussian interaction profile (GIP) kernel and cosine similarity. MVGNCDA utilizes multi-view GCNs to extract local node embeddings of diseases and circRNAs in the context of multisource information. Then, we construct a heterogeneous network utilizing integrated similarity and verified circRNA-disease associations, which is subsequently used to learn global node embeddings. Furthermore, the final fused local and global node embeddings are decoded to evaluate the circRNA-disease associations using a bilinear decoder. The fivefold cross-validation results demonstrate that MVGNCDA outperforms existing methods across five public datasets. Moreover, case study also confirms that MVGNCDA is capable of efficiently identifying unknown circRNA-disease associations.

关键词： Multi-view feature aggregation CircRNA-disease association graph convolutional network node2vec network embedding Deep learning

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LG-STSGCN: Long-Term Gated Pedestrian Trajectory Prediction Based on Spatial-Temporal Synchronous graph convolutional network

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2025年第4期9卷

作者： Chen, Yang Guo, Juwei Wang, Ke Yang, Dongfang Yan, Xu Qiu, Lihong Chongqing Changan Automobile Co Ltd State Key Lab Intelligent Vehicle Safety Technol Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Changan & Chongqing Changan Technol Res & Innovat Ctr Chongqing 400044 Peoples R China

Pedestrian trajectory prediction is fundamental research in many practical applications, such as video surveillance, autonomous vehicles, and robotic systems. However, the existing methods do not capture the spatial-temporal correlation of pedestrians well and simultaneously, as well as do not learn the temporal global interaction features of pedestrians effectively. To address these issues, we propose a long-term gated pedestrian trajectory prediction model based on spatial-temporal synchronous graph convolutional network. The proposed method consists of three components. First, we construct a localized spatial-temporal graph to characterize the temporal information, spatial information and spatial-temporal correlation information among pedestrians in the pedestrian trajectory prediction fully. Then, we introduce a gated mechanism into the temporal convolutional network, in parallel with the gated spatial-temporal synchronous graph convolutional network, in order to improve the model's ability to capture the global correlation of spatial-temporal data. Finally, we add random noise and use a diversity loss function to train and predict trajectories. We conduct experiments on ETH and UCY datasets and the proposed method is proved to outperform previous approaches.

关键词： Logic gates Trajectory Pedestrians Predictive models Convolution Decoding Correlation graph convolutional networks Training Noise Sensor applications environment perception graph convolutional network pedestrian trajectory prediction spatial-temporal correlation information temporal convolutional networks (TCN)

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Multi-view multi-level contrastive graph convolutional network for cancer subtyping on multi-omics data

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BRIEFINGS IN BIOINFORMATICS 2025年第1期26卷 bbaf043页

作者： Yang, Bo Cui, Chenxi Wang, Meng Ji, Hong Gao, Feiyue Xian Polytech Univ Sch Comp Sci Xian 710048 Peoples R China Xian Polytech Univ Shaanxi Key Lab Clothing Intelligence Xian 710048 Peoples R China

Cancer is a highly diverse group of diseases, and each type of cancer can be further divided into various subtypes according to specific characteristics, cellular origins, and molecular markers. Subtyping helps in tailoring treatment and prognosis accuracy. However, the existing studies are more concerned with integrating different omics data to discover potential connections, but ignoring the relationships between consensus information and individual information within each omics level during the integration process. To this end, we propose a novel fusion-free method called multi-view multi-level contrastive graph convolutional network (M$<^>{2}$CGCN) for cancer subtyping. M$<^>{2}$CGCN learns multi-level features, i.e. high-level and low-level features, respectively. The low-level features from each view capture the intrinsic information in each omics by reconstruction of node attribute and graph structures. The high-level features achieve cancer subtyping via contrastive learning. Comprehensive experiments were performed on 34 multi-omics cancer datasets. The findings indicate that M$<^>{2}$CGCN achieves results comparable to or surpassing many state-of-the-art methods.

关键词： contrastive learning graph convolutional network cancer subtype multi-omics data

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MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia

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BRAIN RESEARCH BULLETIN 2025年 221卷 111199-111199页

作者： Wang, Haiyuan Peng, Runlin Huang, Yuanyuan Liang, Liqin Wang, Wei Zhu, Baoyuan Gao, Chenyang Guo, Minxin Zhou, Jing Li, Hehua Li, Xiaobo Ning, Yuping Wu, Fengchun Wu, Kai South China Univ Technol Sch Biomed Sci & Engn Guangzhou Int Campus Guangzhou 511442 Peoples R China South China Univ Technol Sch Mat Sci & Engn Guangzhou 510006 Peoples R China Guangzhou Med Univ Dept Psychiat Affiliated Brain Hosp Guangzhou 510370 Peoples R China Guangdong Engn Technol Res Ctr Translat Med Mental Guangzhou 510370 Peoples R China Guangdong Engn Technol Res Ctr Diag & Rehabil Deme Guangzhou 510500 Peoples R China South China Univ Technol Natl Engn Res Ctr Tissue Restorat & Reconstruct Guangzhou 510006 Peoples R China South China Univ Technol Guangdong Prov Key Lab Biomed Engn Guangzhou 510006 Peoples R China New Jersey Inst Technol Dept Biomed Engn Newark NJ USA Guangzhou Med Univ Key Lab Neurogenet & Channelopathies Guangdong Pro Guangzhou 510370 Peoples R China Guangzhou Med Univ Minist Educ China Guangzhou 510370 Peoples R China Tohoku Univ Inst Dev Aging & Canc Dept Aging Res & Geriatr Med Sendai 9808575 Japan

The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these studies ignored the cooperative interactions and topological attributes of multi-omics networks. In this study, we constructed three types of brain graphs (BGs), three types of gut graphs (GGs), and nine types of brain-gut combined graphs (BGCGs) for each individual. We proposed a novel methodology of multi-omics graph convolutional network (MO-GCN) with an attention mechanism to construct a classification model by integrating all BGCGs. We also identified important brain and gut features using the Topk pooling layer and analyzed their correlations with the Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery (MCCB) scores. The results showed that the novel MO-GCN model using BGCGs outperformed the GCN models using either BGs or GGs. In particular, the accuracy of the best model by 5fold cross-validation reached 84.0 %. Interpretability analysis revealed that the top 10 important brain features were primarily from the hippocampus, olfactory, fusiform and pallidum, which were involved in the brain systems of memory, learning and emotion. The top 10 important gut features were primarily from Dorea, Ruminococcus, Subdoligranulum and Clostridium, etc. Moreover, the important brain and gut features were significantly correlated with the PANSS and MCCB scores, respectively. In conclusion, the MO-GCN can effectively improve the classification performance and provide a potential gut microbiota-brain perspective for the understanding of schizophrenia.

关键词： Schizophrenia Multi-omics Brain network Gut network Classification graph convolutional network

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ScAGCN: graph convolutional network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2025年 1-10页

作者： Zhu, Xiaoshu Zhao, Liquan Teng, Fei Meng, Shuang Xie, Miao Guilin Univ Elect Technol Sch Comp & Informat Secur Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guangxi Normal Univ Sch Comp Sci & Engn Guilin 541006 Peoples R China Yulin Normal Univ Sch Comp Sci & Engn Yulin 537000 Peoples R China

With the development of single-cell RNA-sequencing (scRNA-seq) technology, scRNA-seq data analysis suffers huge challenges due to large scale, high dimensionality, high noise, and high sparsity. To achieve accurately embedded representation in the large-scale scRNA-seq data, we try to design a novel graph convolutional network with an adaptive aggregation mechanism. Based on the assumption that the aggregation order of different cells would be different, a graph convolutional network with an adaptive aggregation-based dimensionality reduction algorithm for scRNA-seq data is developed, named scAGCN. In scAGCN, a preprocessing consisting of quality control and feature selection is implemented. Then, an approximate nearest neighbor graph is rapidly constructed. Finally, a graph convolutional network with an adaptive aggregation mechanism is constructed, in which the neighborhood selection strategy based on node distribution and similarity boxplots is designed, and the aggregation function is optimized by defining a similarity measurement between neighborhood nodes and the central node. The results show that scAGCN outperforms existing dimensionality reduction methods on 15 real scRNA-seq datasets, especially in 10 large-scale scRNA-seq datasets.

关键词： Dimensionality reduction Single-cell RNA-seq graph convolutional network Aggregation optimization Tolerance class Similarity measurement Hierarchical clustering

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Aspect Term Extraction via Dynamic Attention and a Densely Connected graph convolutional network 21st

Aspect Term Extraction via Dynamic Attention and a Densely C...

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21st Pacific Rim International Conference on Artificial Intelligence

作者： Sun, Xin Mi, Yongqing Liu, Jia Li, Hongao Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Minist Educ Ctr Educ Technol & Resource Dev Beijing Peoples R China NCET Natl Ctr Educ Technol Beijing 100031 Peoples R China

ISBN: (纸本)9789819601158;9789819601165

Aspect term extraction is a crucial step in aspect-level sentiment analysis, significantly affecting the accuracy of sentiment classification. Therefore, improving the precision of aspect term extraction is vital for enhancing the performance of sentiment analysis. The limitations of existing methods include inadequate consideration of syntactic information and inter-word dependencies, as well as the challenge of mitigating weight noise during dependency tree conversion. To address these issues, we propose an aspect term extraction approach that leverages dynamic attention and graph convolutional network. Our method utilizes a densely connected graph convolutional network to capture dependency information between distant terms, thereby enriching vector semantics. Furthermore, it integrates a dynamic attention mechanism informed by dependency parsing to highlight critical dependencies and mitigate noise interference. We benchmark our model against state-of-the-art approaches on four widely used public datasets. The results indicate that our proposed method significantly enhances the performance of aspect term extraction. Specifically, our model improves upon baseline models on the Lap14 and Rest15 datasets, with increases in macro-F1 scores of 0.45, and 0.04, respectively.

关键词： Aspect term extraction graph convolutional network Dynamic attention mechanism

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A graph convolutional network approach for hyperspectral image analysis of blueberries physiological traits under drought stress

SMART AGRICULTURAL TECHNOLOGY

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SMART AGRICULTURAL TECHNOLOGY 2025年 10卷

作者： Rahman, Md. Hasibur Busby, Savannah Hanif, Sajid Maruf, Md Mesbahul Ahmad, Faraz Ru, Sushan Sanz-Saez, Alvaro Zheng, Jingyi Rehman, Tanzeel U. Auburn Univ Dept Biosyst Engn Auburn AL 36849 USA Auburn Univ Dept Hort Auburn AL 36849 USA Auburn Univ Dept Crop Soil & Environm Sci Auburn AL 36849 USA Auburn Univ Dept Math & Stat Auburn AL 36849 USA

Blueberries are extremely susceptible to drought due to their shallow root systems and limited water regulation capabilities. Climate change exacerbates drought stress in major blueberry production regions, which affect key physiological traits, such as leaf water content (LWC), photosynthesis (A), stomatal conductance (gs), electron transport rate (ETR), photosystem II efficiency (phi PSII) and transpiration rate (E). Current phenotyping methods for measuring these physiological traits are time-consuming and labor-intensive as well as limited by the need for specialized equipment. To address this, a high-throughput phenotyping (HTPP) platform integrated with hyperspectral camera and a novel graph convolutional network (GCN)-based model, Plant-GCN, was developed to predict physiological traits of blueberry plants under drought stress. Spectral reflectance obtained from the hyperspectral images were transformed into a graph representation, with each plant represented as a node, spectral reflectance as node features, and edges defined by spectral similarities. The Plant-GCN model utilizes graph convolutional layers that aggregate information from neighboring nodes, effectively capturing complex interactions in the spectral signature and enhancing the prediction of physiological traits. Plant-GCN achieved a coefficient of determination (R2) of 0.89 for LWC, 0.94 for A, 0.89 for gs, 0.92 for ETR, 0.93 for phi PSII and 0.89 for E on the test dataset. The performance of the proposed Plant-GCN model was compared with multilayer perceptron (MLP), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), and it consistently outperformed all these models as well as data published in other reports. The high-throughput phenotyping system enabled efficient large-scale data collection, while the Plant-GCN model captured long-range spectral relationships significantly improved the prediction of physiological traits. The high predictability o

关键词： Drought tolerance High-throughput phenotyping Hyperspectral imaging graph convolutional network Plant physiology Predictive analysis

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Adversarial graph convolutional network Hashing for Cross-Modal Retrieval 7th

Adversarial Graph Convolutional Network Hashing for Cross-Mo...

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8th Asia Pacific Web and Web-Age Information Management Joint International Conference on Web and Big Data

作者： Lu, Bo Zhao, Tianbao Liang, Guiyuan Li, Jiaming Duan, Xiaodong Dalian Minzu Univ Dalian 116650 Peoples R China

ISBN: (纸本)9789819600540;9789819600557

graph-based hashing has recently emerged as a prominent research area due to the rapid expansion of multimodal datasets. Most existing graph-based methods rely on graph convolutional networks (GCNs) as a guiding mechanism, primarily because of the unique characteristics of cross-modal retrieval tasks. However, these methods often encounter several challenges: 1) They struggle to preserve the inherent similarities within the original datasets. 2) They frequently employ intractable binary quadratic programs, which limits their scalability when dealing with extensive data volumes. In this paper, we introduce an innovative framework termed Adversarial graph convolutional network Hashing (AGCNH), which leverages dual-cycle Generative Adversarial networks (GANs) alongside GCNs. This framework incorporates two asymmetric graph layers and a standard graph layer, effectively addressing the issue of excessive parameterization in GCNs and enabling the direct generation of binary codes. To demonstrate the effectiveness of our proposed method, we have conducted experiments on four widely used datasets, thereby validating the robustness and applicability of our approach.

关键词： Cross-Modal Retrieval Adversarial graph convolutional network Hashing

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Peer Collaboration in DBLP Using graph convolutional network

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SN Computer Science 2025年第1期6卷 1-7页

作者： Kumar, Chintoo Dar, Showkat Ahmad Batchu, Charishma Singamaneni, Kundan Sai Panguluri, Venkata Lakshmi Alekhya Department of Artificial Intelligence and Data Science GITAM Karnataka Bengaluru 562163 India Department of Computer Science and Engineering GITAM Karnataka Bengaluru 562163 India

The increasing popularity of graph-based neural network architectures plays a pivotal role in providing promising results in applications, viz., Friendship networks, Co-authorship networks, Product recommendations, etc. Many machine learning tasks can be addressed when dealing with graph representation, i.e., graph classification, node classification, link prediction, graph embedding, etc. The traditional rule-based methods have many drawbacks in comparison to graph representation learning, such as it requires manual feature engineering and struggles with unseen cases (generalizability), etc., in the case of rule-based methods. At the same time, graph representation learning overcomes those issues profoundly. The advent of graph-based deep neural network architectures inspired us to complete a graph classification problem to explore clusters of researchers who worked together in academic publications. We built the model by using a graph convolutional network to solve a multi-classification problem. We conducted a comparative study to check the efficacy of graph representation learning by GCNs on the DBLP dataset. A comparative study shows that the built model outperforms the baseline models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DBLP graph convolutional network graph mining Social networks

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