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graph convolutional network based on self-attention variational autoencoder and capsule contrastive learning for aspect-based sentiment analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 279卷

作者： Wang, Xinyue Liu, Long Chen, Zhuo Wang, Haiyan Yu, Bin Qingdao Univ Sci & Technol Sch Data Sci Qingdao 266061 Peoples R China Qingdao Univ Sci & Technol Sch Foreign Languages Qingdao 266061 Peoples R China Univ Sci & Technol China Sch Artificial Intelligence & Data Sci Hefei 230026 Peoples R China

Aspect-based sentiment analysis (ABSA) predicts sentiment polarity by aligning aspect words with their matching sentiment words in sentences at a fine-grained level. Previous methods have limitations, such as over-reliance on syntactic trees ignoring semantic and global information, and difficulty in solving long-distance dependency problems. In response to these issues, this article puts forward a hybrid graph convolutional network (GCN) called SVCCL-GCN, which combines an improved variational autoencoder (VAE) and a contrastive learning module. First, construct dependency graph structures using syntactic dependency trees to obtain explicit syntactic dependency information. Then, a VAE module based on the self-attention mechanism is devised to acquire both global and local information of the data, and it is beneficial for capturing the explicit associations between aspect terms and relevant contexts to solve the long-distance dependency problems. Finally, so as to stimulate the model's capacity to flexibly learn comprehensive semantic information and emotional feature information between different words, a contrastive learning loss based on the capsule network is constructed by taking the output of the capsule network as the contrastive feature for unsupervised contrastive learning. The extensive experimental findings on five benchmark datasets demonstrate that SVCCL-GCN productively improves the accuracy in distinguishing sentiment polarity. The source codes and datasets are available at https://github. com/YuBinLab-QUST/SVCCL-GCN/.

关键词： Aspect-based sentiment analysis graph convolutional network Variational autoencoder Capsule contrastive learning

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graph convolutional network-based unsupervised learning of percolation transition

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COMPUTATIONAL MATERIALS SCIENCE 2025年 248卷

作者： Cha, Moon-Hyun Hwang, Jeongwoon Brown Univ Sch Engn Providence RI 02912 USA Samsung Elect Innovat Ctr CSE Team Hwaseong 18448 South Korea Chonnam Natl Univ Dept Phys Educ Gwangju 61186 South Korea

In this study, we address the challenge of detecting percolation phase transitions using unsupervised machine learning methods. Unlike the Ising model, where machine learning has shown success, percolation problems have proven more difficult due to the lack of direct interactions between sites. We propose a graph convolutional network (GCN) approach combined with the confusion method to identify percolation transitions and estimate percolation thresholds. Various pooling methods, including state-of-the-art differentiable pooling, are evaluated for their effectiveness in aggregating global information. Our experiments on 10 x 10 lattices demonstrate that the physical-intuition-based coarse-graining method significantly outperforms other pooling techniques, providing more accurate and reliable predictions. We further validate the model's performance on larger systems, confirming its robustness and applicability. Our findings highlight the potential of GCNs with appropriate pooling methods in studying percolation transitions and suggest promising direction for future applications in condensed matter physics.

关键词： Percolation transition Unsupervised learning graph convolutional network

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graph convolutional network for fast video summarization in compressed domain

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NEUROCOMPUTING 2025年 617卷

作者： Yeh, Chia-Hung Lien, Chih-Ming Zhan, Zhi-Xiang Tsai, Feng-Hsu Chen, Mei-Juan Natl Taiwan Normal Univ Dept Elect Engn Taipei 106308 Taiwan Natl Sun Yat sen Univ Dept Elect Engn Kaohsiung Taiwan KKCompany Platform & Growth Ctr Adv Streaming Technol Div Taipei 115020 Taiwan Natl Dong Hwa Univ Dept Elect Engn Hualien 974301 Taiwan

Video summarization is the process of generating a concise and representative summary of a video by selecting its most important frames. It plays a vital role in the video streaming industry, allowing users to quickly understand the overall content of a video without watching it in its entirety. Most existing video summarization methods require fully decoding the video stream and extracting the features with a pre-trained deep learning model in the pixel domain, which is time-consuming and computationally expensive. To address this issue, this paper proposes a novel method called graph convolutional network-based Compressed-domain Video Summarization (GCNCVS), which directly exploits the compressed-domain information and leverages graph convolutional network to learn temporal relationships between frames, thereby enhancing its ability to capture contextual and valuable information when generating summarized videos. To evaluate the performance of GCNCVS, we conduct experiments on two benchmark datasets, SumMe and TVSum. Experimental results demonstrate that our method outperforms existing methods, achieving an average F-score of 53.5% on the SumMe dataset and 72.3% on the TVSum dataset. Additionally, the proposed method shows Kendall's tau correlation coefficient of 0.157 and Spearman's rho correlation coefficient of 0.205 on the TVSum dataset. Our method also significantly reduces computational time, which enhances the feasibility of video summarization in video streaming environments.

关键词： Video Summarization Video Compression graph convolutional network Compressed Domain

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Crash risk prediction using sparse collision data: Granger causal inference and graph convolutional network approaches

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 259卷

作者： Hu, Junjie Bai, Jun Yang, Jiayu Lee, Jaeyoung Jay Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

Applying deep learning techniques in predicting traffic crashes provides a possibility to achieve the vision of "zero fatalities" by implementing a proactive safety countermeasure. The overly sparse distribution of crashes hinders high-precision prediction of crash risks in urban areas. The lack of effective crash association analysis also reduces the interpretability of the prediction model. To address these gaps, this paper proposes a novel method, Priori Spatial and Temporal Causal graph convolutional network (PST-CGCN) for predicting crash risk based on causal inference and graph convolutional network. In PST-CGCN, a data enhancement strategy utilizing historic priori spatial and temporal knowledge is employed to improve the distinguishability between crash data, then a graph convolutional network based on causal association is designed. The focus of this study is to shift attention from using multiple data sources to predict traffic risk to exploring the intrinsic temporal and spatial causal relationship between traffic crashes in different regions. Results from the NYC case study show that PST-CGCN model performs best compared with seven baseline models on three indicators, identifying high crash risk periods and areas in short-term forecasting. The results of ablation experiments on the PST-CGCN demonstrate the rationality of each component in the framework. Besides, gradient analysis combined with causal association is applied to explore directed causal relationships between regions, which enhances the model's interpretability.

关键词： Crash prediction graph convolutional network Granger test Crash data enhancement Model interpretability

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Mineral Prospectivity Mapping Based on a Novel Self-Ensembling graph convolutional network

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MATHEMATICAL GEOSCIENCES 2025年第4期57卷 629-656页

作者： Lou, Yonghang Liu, Yue China Univ Geosci Fac Earth Resources Wuhan 430074 Peoples R China China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 430074 Peoples R China

The graph convolutional network (GCN) has proven to be a powerful tool for exploration criterion identification and mineral prospectivity analysis because of its excellent ability to integrate and capture complex spatial geo-anomalies associated with mineralization. However, the graph convolution of the GCN is essentially a special form of Laplacian smoothing that can result in the over-smoothing of output features. This over-smoothing can undermine the reliability of mineral prospectivity mapping (MPM), which aims to identify mineralization-related geo-anomalies typically characterized by a series of anomalous values in the GCN model. To solve this problem, we introduce a novel deep learning model, the self-ensembling graph convolutional network (SEGCN), which integrates the mean teacher model with a GCN for MPM by fully learning unlabelled geological features. To illustrate the superiority of the SEGCN model, a case study for tungsten polymetallic prospectivity analysis was carried out in the Nanling belt, China. The SEGCN model was used to build a classification model based on the fusion of multiple geospatial datasets, including geochemical data and data on faults and rock masses. The SEGCN model was compared with conventional GCN, convolutional neural network (CNN), random forest (RF), and support vector machine (SVM) models in terms of their classification performance and predictive accuracy, and was evaluated via receiver operating characteristic (ROC) curves and prediction-area (P-A) plots. The results demonstrated that the SEGCN model significantly outperformed the other four models in mapping tungsten polymetallic prospectivity in the Nanling region of China. We concluded that the SEGCN model can establish an efficient, robust, and high-performance classification model to effectively recognize favourable mineralization targets from multi-source geo-anomalies, thereby suppressing the over-smoothing effect in the GCN.

关键词： Mineral prospectivity mapping graph convolutional network Machine learning Mineral exploration Multi-source geospatial data fusion

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Multi-information Fusion graph convolutional network for cancer driver gene identification

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PATTERN RECOGNITION 2025年 165卷

作者： Hu, Die Liu, Yanbei Wang, Xiao Geng, Lei Zhang, Fang Xiao, Zhitao Lin, Jerry Chun-Wei Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China Tiangong Univ Sch Life Sci Tianjin 300387 Peoples R China Tianjin Key Lab Optoelect Detect Technol & Syst Tianjin 300387 Peoples R China Beihang Univ Sch Software Beijing 100191 Peoples R China Silesian Tech Univ Dept Distributed Syst & IT Devices PL-44100 Gliwice Poland

Cancer is generally thought to be caused by the accumulation of mutations in driver genes. The identification of cancer driver genes is crucial for cancer research, diagnosis and treatment. Despite existing methods, challenges remain in comprehensively learning of the attributes and intricate interactions of genetic data. We propose a novel Multi-information Fusion graph convolutional network (MF-GCN) for cancer driver gene identification, based on multi-omics pan-cancer data and Gene Regulatory network (GRN) data. Directed topological and attribute graph networks learn gene interactions and self-attribute information, while a common graph network captures consistency between topology and attributes. An attention mechanism adaptively fuses these information with importance weights to identify cancer driver genes. Experimental results showed that MF-GCN can effectively identify cancer driver genes across three GRN datasets, with AUROC and AUPRC improvements of 2.66% and 2.69%, respectively, compared with the state-of-the-art approaches.

关键词： Cancer driver gene identification graph convolutional network Multi-information fusion Attention mechanism

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Prior knowledge-guided multi-information graph convolutional network for driver drowsiness detection

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 275卷

作者： Wei, Feng Yang, Jucheng Wang, Yuan Lin, Liang Zhang, Haibin Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin 300457 Peoples R China Tianjin Univ Sci & Technol Coll Mech Engn Tianjin 300457 Peoples R China Xidian Univ Sch Cyber Secur Xian 710126 Peoples R China

Recently, driver drowsiness detection has received significant research attention, primarily due to the escalating number of road accidents caused by drowsiness driving. To tackle this issue, computer vision has been employed to detect drowsy states by analyzing drivers' facial expressions. However, existing methods based on computer vision often rely on one or two facial regions (i.e., eyes, and mouth) to detect drowsiness. This approach leads to a failure to account for the driver individual differences. Moreover, although facial regions are highly structured, existing methods employ non-structural architectures to model the drowsiness feature space, lacking guidance from prior knowledge and resulting in the loss of high-level detail features. To this end, we propose a prior knowledge-guided multi-information graph convolutional network (MIGCN) to address these issues for driver drowsiness detection. Compared to driver drowsiness detection methods based on CNN models, the structural MIGCN can effectively learn spatial facial features, enhancing feature representation. Additionally, the core of the proposed MIGCN consists of three modules: the multi-source features extraction module (MSFE), the multi-information representation module (MIRM), and the multi-information GCN and fusion module (MIGCN-F). The MSFE extracts multi-source features from five prior knowledge and entire facial regions to enhance the drowsiness feature space. These prior knowledge provide detailed information, and the entire facial region offers global high-level information. The MIRM injects class, attention, and temporal information into the multi-source features and also provides mean features from the same class, resulting in enriched multi-information features. Together, the MSFE and MIRM address the issue of drivers individual differences. Additionally, we design a task-driven novel MIGCN-F module, whose nodes are composed of multi-information features. This design not only preserves the

关键词： Drowsiness detection Prior knowledge Driver individual differences Multi-source/information features graph convolutional network

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GazeGCN: Gaze-aware graph convolutional network for Text Classification

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NEUROCOMPUTING 2025年 611卷

作者： Wang, Bingbing Liang, Bin Bai, Zhixin Yang, Min Gui, Lin Xu, Ruifeng Harbin Inst Technol Shenzhen Guangdong Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Harbin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China Kings Coll London London England Guangdong Prov Key Lab Novel Secur Intelligence Te Shenzhen Guangdong Peoples R China

graph convolutional networks (GCNs) are capable of capturing contextual relationships in text classification. In this paper, we propose a novel Gaze-aware graph convolutional network (GazeGCN) for text classification, where the gaze signals from humans are incorporated into the GCN architecture, empowering our GazeGCN to use gaze information that can be shared across all data to model the intricate relationships between words and documents. To be specific, we first build a gaze prediction model to obtain five gaze signals to form the gaze distribution of each word. Then, Wasserstein Distance is employed to derive the gaze-aware word-word weight by calculating the gaze distribution between words, so as to improve the capture of luxuriant and similar relationships between words. Furthermore, to enhance the extraction of contextual syntactic information, we introduce a Gaze-enhanced TF-IDF method integrating gaze signals and term frequency-inverse document frequency (TF-IDF) for gaze-aware word-document weight derivation, thus making up for the lack of TF-IDF that does not consider syntactic information. Afterward, the rich source of graph edge information including gaze-aware word-word weight and gaze-aware word-document weight is incorporated to construct graphs, so as to leverage the relationship of word-word and word-document. Experiments show encouraging results on seven benchmark datasets that our approach outperforms the state-of-the-art baseline methods.

关键词： Gaze signals graph convolutional network Wasserstein distance Gaze prediction model

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Semantic enhanced bi-syntactic graph convolutional network for aspect-based sentiment analysis

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INFORMATION SCIENCES 2025年 712卷

作者： Xiao, Junyang Xue, Yun Li, Fenghuan South China Normal Univ Sch Elect & Informat Engn Guangdong Prov Key Lab Quantum Engn & Quantum Mat Sch Microelect Foshan 528225 Peoples R China Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China

Previous work on fine-grained sentiment analysis focuses on establishing the semantic correlations between words by means of attention mechanisms. More recently, effects of syntax-based models, applying graph convolution operation over dependency trees, are highlighted due to their superiority. However, these methods still have deficiencies. For one thing, little aspect-specific information is considered during semantic modeling of contextual words, which introduces irrelevant noise toward the aspect. For another, current syntax-based approaches either ignore the syntactic constituent knowledge, or fail to maintain the syntactic information from the reconstructed constituent tree. As such, no relation among words, phrases and clauses is built. In this work, a Semantic Enhanced Bi-Syntax graph convolutional network (SEBS-GCN) is proposed to enhance semantics of context to the aspect, and capture the sentiment relevance among words, phrases and clauses. Specifically, we devise an aspect-aware gated mechanism to obtain the aspect-aware feature, based on the semantic correlations between the specific aspect and its contexts. Furthermore, the syntax information of the constituent tree is sufficiently exploited to analyze the hierarchical structure and the logical relation among words, phrases and clauses, based on which to capture the sentiment clues of the aspect.

关键词： graph convolutional network Constituent tree Enhanced semantics Bi-syntax Aspect-based sentiment analysis

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RWGCN: Random walk graph convolutional network for group activity recognition

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APPLIED INTELLIGENCE 2025年第6期55卷 1-18页

作者： Kang, Junpeng Zhang, Jing Chen, Lin Zhang, Hui Zhuo, Li Beijing Univ Sci & Technol Sch Informat Engn Beijing Peoples R China Beijing Univ Technol Key Lab Computat Intelligence & Intelligent Syst Beijing 100124 Peoples R China

Group activity recognition can remarkably improve the understanding of video content by analyzing human behaviors and activities in videos. We propose a random walk graph convolutional network (RWGCN) for group activity recognition. (1) Considering the limitation of the convolutional structure to the visual information of group activities, the position feature extraction module is used to compensate for the loss of visual information. (2) A graph convolutional network (GCN) with distance-adaptive edge relations is constructed using individuals as graph nodes to identify the intrinsic relationships among the individuals in the group activities. (3) A Levy flight random walk mechanism is introduced into the GCN to obtain information from different nodes and integrate the previous position information to recognize group activity. Extensive experiments on the publicly available CAD, CAE datasets, and self-built BJUT-GAD dataset show that our RWGCN achieves MPCA of 95.49%, 94.82%, and 96.02%, respectively, which provides a better competitiveness in group activity recognition compared to other methods.

关键词： Group activity recognition Random walk graph convolutional network Position information Levy flight

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