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Artykuły w czasopismach na temat „Graph and Multi-view Memory Attention”

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Data publikacji: 6 września 2023

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1

Ai, Bing, Yibing Wang, Liang Ji, et al. "A graph neural network fused with multi-head attention for text classification." Journal of Physics: Conference Series 2132, no. 1 (2021): 012032. http://dx.doi.org/10.1088/1742-6596/2132/1/012032.

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Abstract Graph neural network (GNN) has done a good job of processing intricate architecture and fusion of global messages, research has explored GNN technology for text classification. However, the model that fixed the entire corpus as a graph in the past faced many problems such as high memory consumption and the inability to modify the construction of the graph. We propose an improved model based on GNN to solve these problems. The model no longer fixes the entire corpus as a graph but constructs different graphs for each text. This method reduces memory consumption, but still retains globa

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Liu, Di, Hui Xu, Jianzhong Wang, Yinghua Lu, Jun Kong, and Miao Qi. "Adaptive Attention Memory Graph Convolutional Networks for Skeleton-Based Action Recognition." Sensors 21, no. 20 (2021): 6761. http://dx.doi.org/10.3390/s21206761.

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Graph Convolutional Networks (GCNs) have attracted a lot of attention and shown remarkable performance for action recognition in recent years. For improving the recognition accuracy, how to build graph structure adaptively, select key frames and extract discriminative features are the key problems of this kind of method. In this work, we propose a novel Adaptive Attention Memory Graph Convolutional Networks (AAM-GCN) for human action recognition using skeleton data. We adopt GCN to adaptively model the spatial configuration of skeletons and employ Gated Recurrent Unit (GRU) to construct an att

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3

Feng, Aosong, Irene Li, Yuang Jiang, and Rex Ying. "Diffuser: Efficient Transformers with Multi-Hop Attention Diffusion for Long Sequences." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12772–80. http://dx.doi.org/10.1609/aaai.v37i11.26502.

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Efficient Transformers have been developed for long sequence modeling, due to their subquadratic memory and time complexity. Sparse Transformer is a popular approach to improving the efficiency of Transformers by restricting self-attention to locations specified by the predefined sparse patterns. However, leveraging sparsity may sacrifice expressiveness compared to full-attention, when important token correlations are multiple hops away. To combine advantages of both the efficiency of sparse transformer and the expressiveness of full-attention Transformer, we propose Diffuser, a new state-of-t

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4

Li, Mingxiao, and Marie-Francine Moens. "Dynamic Key-Value Memory Enhanced Multi-Step Graph Reasoning for Knowledge-Based Visual Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 10983–92. http://dx.doi.org/10.1609/aaai.v36i10.21346.

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Knowledge-based visual question answering (VQA) is a vision-language task that requires an agent to correctly answer image-related questions using knowledge that is not presented in the given image. It is not only a more challenging task than regular VQA but also a vital step towards building a general VQA system. Most existing knowledge-based VQA systems process knowledge and image information similarly and ignore the fact that the knowledge base (KB) contains complete information about a triplet, while the extracted image information might be incomplete as the relations between two objects a

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5

Jung, Tae-Won, Chi-Seo Jeong, In-Seon Kim, Min-Su Yu, Soon-Chul Kwon, and Kye-Dong Jung. "Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud." Sensors 22, no. 21 (2022): 8166. http://dx.doi.org/10.3390/s22218166.

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Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation. Graph Convolutional Neural Networks (GCNs), as a representative method in GNNs, in the context of computer vision, utilize conventional Convolutional Neural Networks (CNNs) to process data supported by graphs. This paper proposes a one-stage GCN approach for 3D object detection and poses estimation by structuring non-linearly distributed points of a graph. Our network provides the required details to analyze, ge

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6

Cui, Wei, Fei Wang, Xin He, et al. "Multi-Scale Semantic Segmentation and Spatial Relationship Recognition of Remote Sensing Images Based on an Attention Model." Remote Sensing 11, no. 9 (2019): 1044. http://dx.doi.org/10.3390/rs11091044.

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A comprehensive interpretation of remote sensing images involves not only remote sensing object recognition but also the recognition of spatial relations between objects. Especially in the case of different objects with the same spectrum, the spatial relationship can help interpret remote sensing objects more accurately. Compared with traditional remote sensing object recognition methods, deep learning has the advantages of high accuracy and strong generalizability regarding scene classification and semantic segmentation. However, it is difficult to simultaneously recognize remote sensing obje

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7

Hou, Miaomiao, Xiaofeng Hu, Jitao Cai, Xinge Han, and Shuaiqi Yuan. "An Integrated Graph Model for Spatial–Temporal Urban Crime Prediction Based on Attention Mechanism." ISPRS International Journal of Geo-Information 11, no. 5 (2022): 294. http://dx.doi.org/10.3390/ijgi11050294.

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Crime issues have been attracting widespread attention from citizens and managers of cities due to their unexpected and massive consequences. As an effective technique to prevent and control urban crimes, the data-driven spatial–temporal crime prediction can provide reasonable estimations associated with the crime hotspot. It thus contributes to the decision making of relevant departments under limited resources, as well as promotes civilized urban development. However, the deficient performance in the aspect of the daily spatial–temporal crime prediction at the urban-district-scale needs to b

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8

Mi, Chunlei, Shifen Cheng, and Feng Lu. "Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks." ISPRS International Journal of Geo-Information 11, no. 3 (2022): 185. http://dx.doi.org/10.3390/ijgi11030185.

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Predicting taxi-calling demands at the urban area level is vital to coordinate the supply–demand balance of the urban taxi system. Differing travel patterns, the impact of external data, and the expression of dynamic spatiotemporal demand dependence pose challenges to predicting demand. Here, a framework using residual attention graph convolutional long short-term memory networks (RAGCN-LSTMs) is proposed to predict taxi-calling demands. It consists of a spatial dependence (SD) extractor, which extracts SD features; an external dependence extractor, which extracts traffic environment-related f

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9

Karimanzira, Divas, Linda Ritzau, and Katharina Emde. "Catchment Area Multi-Streamflow Multiple Hours Ahead Forecast Based on Deep Learning." Transactions on Machine Learning and Artificial Intelligence 10, no. 5 (2022): 15–29. http://dx.doi.org/10.14738/tmlai.105.13049.

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Modeling of rainfall-runoff is very critical for flood prediction studies in decision making for disaster management. Deep learning methods have proven to be very useful in hydrological prediction. To increase their acceptance in the hydrological community, they must be physic-informed and show some interpretability. They are several ways this can be achieved e.g. by learning from a fully-trained hydrological model which assumes the availability of the hydrological model or to use physic-informed data. In this work we developed a Graph Attention Network (GAT) with learnable Adjacency Matrix co

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10

Wang, Changhai, Jiaxi Ren, and Hui Liang. "MSGraph: Modeling multi-scale K-line sequences with graph attention network for profitable indices recommendation." Electronic Research Archive 31, no. 5 (2023): 2626–50. http://dx.doi.org/10.3934/era.2023133.

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<abstract><p>Indices recommendation is a long-standing topic in stock market investment. Predicting the future trends of indices and ranking them based on the prediction results is the main scheme for indices recommendation. How to improve the forecasting performance is the central issue of this study. Inspired by the widely used trend-following investing strategy in financial investment, the indices' future trends are related to not only the nearby transaction data but also the long-term historical data. This article proposes the MSGraph, which tries to improve the index ranking p

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11

Ma, Xinwei, Yurui Yin, Yuchuan Jin, Mingjia He, and Minqing Zhu. "Short-Term Prediction of Bike-Sharing Demand Using Multi-Source Data: A Spatial-Temporal Graph Attentional LSTM Approach." Applied Sciences 12, no. 3 (2022): 1161. http://dx.doi.org/10.3390/app12031161.

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As a convenient, economical, and eco-friendly travel mode, bike-sharing greatly improved urban mobility. However, it is often very difficult to achieve a balanced utilization of shared bikes due to the asymmetric spatio-temporal user demand distribution and the insufficient numbers of shared bikes, docks, or parking areas. If we can predict the short-run bike-sharing demand, it will help operating agencies rebalance bike-sharing systems in a timely and efficient way. Compared to the statistical methods, deep learning methods can automatically learn the relationship between the inputs and outpu

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12

Lin, Chun, Yijia Xu, Yong Fang, and Zhonglin Liu. "VulEye: A Novel Graph Neural Network Vulnerability Detection Approach for PHP Application." Applied Sciences 13, no. 2 (2023): 825. http://dx.doi.org/10.3390/app13020825.

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Following advances in machine learning and deep learning processing, cyber security experts are committed to creating deep intelligent approaches for automatically detecting software vulnerabilities. Nowadays, many practices are for C and C++ programs, and methods rarely target PHP application. Moreover, many of these methods use LSTM (Long Short-Term Memory) but not GNN (Graph Neural Networks) to learn the token dependencies within the source code through different transformations. That may lose a lot of semantic information in terms of code representation. This article presents a novel Graph

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13

Liu, Daizong, Xiaoye Qu, Xing Di, Yu Cheng, Zichuan Xu, and Pan Zhou. "Memory-Guided Semantic Learning Network for Temporal Sentence Grounding." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1665–73. http://dx.doi.org/10.1609/aaai.v36i2.20058.

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Temporal sentence grounding (TSG) is crucial and fundamental for video understanding. Although existing methods train well-designed deep networks with large amount of data, we find that they can easily forget the rarely appeared cases during training due to the off-balance data distribution, which influences the model generalization and leads to unsatisfactory performance. To tackle this issue, we propose a memory-augmented network, called Memory-Guided Semantic Learning Network (MGSL-Net), that learns and memorizes the rarely appeared content in TSG task. Specifically, our proposed model cons

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14

Wu, Jie, Ian G. Harris, and Hongzhi Zhao. "GraphMemDialog: Optimizing End-to-End Task-Oriented Dialog Systems Using Graph Memory Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11504–12. http://dx.doi.org/10.1609/aaai.v36i10.21403.

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Effectively integrating knowledge into end-to-end task-oriented dialog systems remains a challenge. It typically requires incorporation of an external knowledge base (KB) and capture of the intrinsic semantics of the dialog history. Recent research shows promising results by using Sequence-to-Sequence models, Memory Networks, and even Graph Convolutional Networks. However, current state-of-the-art models are less effective at integrating dialog history and KB into task-oriented dialog systems in the following ways: 1. The KB representation is not fully context-aware. The dynamic interaction be

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15

Liu, Yuanxin, Delei Tian, and Bin Zheng. "Non-communicating Decentralized Multi-robot Collision Avoidance in Grid Graph Workspace based on Dueling Double Deep Q-Network." Journal of Physics: Conference Series 2456, no. 1 (2023): 012015. http://dx.doi.org/10.1088/1742-6596/2456/1/012015.

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Abstract This paper designs a motion rule suitable for grid environment and proposes a multi-robot autonomous obstacle avoidance method based on deep reinforcement learning. The training and validation of the method are done under the Stage simulation platform based on ROS operating system. During the training process, the robot uses Lidar to obtain the surrounding state information and generates actions based on the state information to obtain rewards, and the robot is guided by the rewards to optimize the strategy. Based on the D3QN algorithm, a new reward function is designed, a proximity p

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16

Su, Guimin, Zimu Zeng, Andi Song, et al. "A General Framework for Reconstructing Full-Sample Continuous Vehicle Trajectories Using Roadside Sensing Data." Applied Sciences 13, no. 5 (2023): 3141. http://dx.doi.org/10.3390/app13053141.

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Vehicle trajectory data play an important role in autonomous driving and intelligent traffic control. With the widespread deployment of roadside sensors, such as cameras and millimeter-wave radar, it is possible to obtain full-sample vehicle trajectories for a whole area. This paper proposes a general framework for reconstructing continuous vehicle trajectories using roadside visual sensing data. The framework includes three modules: single-region vehicle trajectory extraction, multi-camera cross-region vehicle trajectory splicing, and missing trajectory completion. Firstly, the vehicle trajec

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17

Hu, Zhangfang, Libujie Chen, Yuan Luo, and Jingfan Zhou. "EEG-Based Emotion Recognition Using Convolutional Recurrent Neural Network with Multi-Head Self-Attention." Applied Sciences 12, no. 21 (2022): 11255. http://dx.doi.org/10.3390/app122111255.

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In recent years, deep learning has been widely used in emotion recognition, but the models and algorithms in practical applications still have much room for improvement. With the development of graph convolutional neural networks, new ideas for emotional recognition based on EEG have arisen. In this paper, we propose a novel deep learning model-based emotion recognition method. First, the EEG signal is spatially filtered by using the common spatial pattern (CSP), and the filtered signal is converted into a time–frequency map by continuous wavelet transform (CWT). This is used as the input data

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18

Ji, Zhanhao, Guojiang Shen, Juntao Wang, Mario Collotta, Zhi Liu, and Xiangjie Kong. "Multi-Vehicle Trajectory Tracking towards Digital Twin Intersections for Internet of Vehicles." Electronics 12, no. 2 (2023): 275. http://dx.doi.org/10.3390/electronics12020275.

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Digital Twin (DT) provides a novel idea for Intelligent Transportation Systems (ITS), while Internet of Vehicles (IoV) provides numerous positioning data of vehicles. However, complex interactions between vehicles as well as offset and loss of measurements can lead to tracking errors of DT trajectories. In this paper, we propose a multi-vehicle trajectory tracking framework towards DT intersections (MVT2DTI). Firstly, the positioning data is unified to the same coordinate system and associated with the tracked trajectories via matching. Secondly, a spatial–temporal tracker (STT) utilizes long

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19

Jin, Yanliang, Jinjin Ye, Liquan Shen, Yong Xiong, Lele Fan, and Qingfu Zang. "Hierarchical Attention Neural Network for Event Types to Improve Event Detection." Sensors 22, no. 11 (2022): 4202. http://dx.doi.org/10.3390/s22114202.

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Event detection is an important task in the field of natural language processing, which aims to detect trigger words in a sentence and classify them into specific event types. Event detection tasks suffer from data sparsity and event instances imbalance problems in small-scale datasets. For this reason, the correlation information of event types can be used to alleviate the above problems. In this paper, we design a Hierarchical Attention Neural Network for Event Types (HANN-ET). Specifically, we select Long Short-Term Memory (LSTM) as the semantic encoder and utilize dynamic multi-pooling and

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20

Tian, Qi, Kun Kuang, Furui Liu, and Baoxiang Wang. "Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11672–80. http://dx.doi.org/10.1609/aaai.v37i10.26379.

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Offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets, which is an important step toward the deployment of multi-agent systems in real-world applications. However, in practice, each individual behavior policy that generates multi-agent joint trajectories usually has a different level of how well it performs. e.g., an agent is a random policy while other agents are medium policies. In the cooperative game with global reward, one agent learned by existing offline MARL often inherits this random policy, jeopardizing the utility

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21

Yang, Shuai, Yueqin Zhang, and Zehua Zhang. "Runoff Prediction Based on Dynamic Spatiotemporal Graph Neural Network." Water 15, no. 13 (2023): 2463. http://dx.doi.org/10.3390/w15132463.

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Runoff prediction plays an important role in the construction of intelligent hydraulic engineering. Most of the existing deep learning runoff prediction models use recurrent neural networks for single-step prediction of a single time series, which mainly model the temporal features and ignore the river convergence process within a watershed. In order to improve the accuracy of runoff prediction, a dynamic spatiotemporal graph neural network model (DSTGNN) is proposed considering the interaction of hydrological stations. The sequences are first input to the spatiotemporal block to extract spati

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Li, Youru, Zhenfeng Zhu, Deqiang Kong, Meixiang Xu, and Yao Zhao. "Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1004–11. http://dx.doi.org/10.1609/aaai.v33i01.33011004.

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Bike-sharing systems, aiming at meeting the public’s need for ”last mile” transportation, are becoming popular in recent years. With an accurate demand prediction model, shared bikes, though with a limited amount, can be effectively utilized whenever and wherever there are travel demands. Despite that some deep learning methods, especially long shortterm memory neural networks (LSTMs), can improve the performance of traditional demand prediction methods only based on temporal representation, such improvement is limited due to a lack of mining complex spatial-temporal relations. To address this

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23

Liu, Lijuan, Mingxiao Wu, Rung-Ching Chen, Shunzhi Zhu, and Yan Wang. "A Hybrid Deep Learning Model for Multi-Station Classification and Passenger Flow Prediction." Applied Sciences 13, no. 5 (2023): 2899. http://dx.doi.org/10.3390/app13052899.

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Multiple station passenger flow prediction is crucial but challenging for intelligent transportation systems. Recently, deep learning models have been widely applied in multi-station passenger flow prediction. However, flows at the same station in different periods, or different stations in the same period, always present different characteristics. These indicate that globally extracting spatio-temporal features for multi-station passenger flow prediction may only be powerful enough to achieve the excepted performance for some stations. Therefore, a novel two-step multi-station passenger flow

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24

Chen, Yun, Chengwei Liang, Dengcheng Liu, et al. "Embedding-Graph-Neural-Network for Transient NOx Emissions Prediction." Energies 16, no. 1 (2022): 3. http://dx.doi.org/10.3390/en16010003.

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Recently, Acritical Intelligent (AI) methodologies such as Long and Short-term Memory (LSTM) have been widely considered promising tools for engine performance calibration, especially for engine emission performance prediction and optimization, and Transformer is also gradually applied to sequence prediction. To carry out high-precision engine control and calibration, predicting long time step emission sequences is required. However, LSTM has the problem of gradient disappearance on too long input and output sequences, and Transformer cannot reflect the dynamic features of historic emission in

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25

Uddin, Md Azher, Joolekha Bibi Joolee, Young-Koo Lee, and Kyung-Ah Sohn. "A Novel Multi-Modal Network-Based Dynamic Scene Understanding." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1 (2022): 1–19. http://dx.doi.org/10.1145/3462218.

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In recent years, dynamic scene understanding has gained attention from researchers because of its widespread applications. The main important factor in successfully understanding the dynamic scenes lies in jointly representing the appearance and motion features to obtain an informative description. Numerous methods have been introduced to solve dynamic scene recognition problem, nevertheless, a few concerns still need to be investigated. In this article, we introduce a novel multi-modal network for dynamic scene understanding from video data, which captures both spatial appearance and temporal

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26

Zhou, Hang, Junqing Yu, and Wei Yang. "Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3769–77. http://dx.doi.org/10.1609/aaai.v37i3.25489.

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Learning discriminative features for effectively separating abnormal events from normality is crucial for weakly supervised video anomaly detection (WS-VAD) tasks. Existing approaches, both video and segment level label oriented, mainly focus on extracting representations for anomaly data while neglecting the implication of normal data. We observe that such a scheme is sub-optimal, i.e., for better distinguishing anomaly one needs to understand what is a normal state, and may yield a higher false alarm rate. To address this issue, we propose an Uncertainty Regulated Dual Memory Units (UR-DMU)

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27

Zhang, Suqi, Xinxin Wang, Wenfeng Wang, Ningjing Zhang, Yunhao Fang, and Jianxin Li. "Recommendation model based on intention decomposition and heterogeneous information fusion." Mathematical Biosciences and Engineering 20, no. 9 (2023): 16401–20. http://dx.doi.org/10.3934/mbe.2023732.

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<abstract> <p>In order to solve the problem of timeliness of user and item interaction intention and the noise caused by heterogeneous information fusion, a recommendation model based on intention decomposition and heterogeneous information fusion (IDHIF) is proposed. First, the intention of the recently interacting items and the users of the recently interacting candidate items is decomposed, and the short feature representation of users and items is mined through long-short term memory and attention mechanism. Then, based on the method of heterogeneous information fusion, the int

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Fang, Ziquan, Lu Pan, Lu Chen, Yuntao Du, and Yunjun Gao. "MDTP." Proceedings of the VLDB Endowment 14, no. 8 (2021): 1289–97. http://dx.doi.org/10.14778/3457390.3457394.

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Traffic prediction has drawn increasing attention for its ubiquitous real-life applications in traffic management, urban computing, public safety, and so on. Recently, the availability of massive trajectory data and the success of deep learning motivate a plethora of deep traffic prediction studies. However, the existing neural-network-based approaches tend to ignore the correlations between multiple types of moving objects located in the same spatio-temporal traffic area, which is suboptimal for traffic prediction analytics. In this paper, we propose a multi-source deep traffic prediction fra

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29

Xie, Yu, Yuanqiao Zhang, Maoguo Gong, Zedong Tang, and Chao Han. "MGAT: Multi-view Graph Attention Networks." Neural Networks 132 (December 2020): 180–89. http://dx.doi.org/10.1016/j.neunet.2020.08.021.

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Yao, Kaixuan, Jiye Liang, Jianqing Liang, Ming Li, and Feilong Cao. "Multi-view graph convolutional networks with attention mechanism." Artificial Intelligence 307 (June 2022): 103708. http://dx.doi.org/10.1016/j.artint.2022.103708.

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Chen, Lei, Jie Cao, Youquan Wang, Weichao Liang, and Guixiang Zhu. "Multi-view Graph Attention Network for Travel Recommendation." Expert Systems with Applications 191 (April 2022): 116234. http://dx.doi.org/10.1016/j.eswa.2021.116234.

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Zhang, Pengyu, Yong Zhang, Jingcheng Wang, and Baocai Yin. "MVMA-GCN: Multi-view multi-layer attention graph convolutional networks." Engineering Applications of Artificial Intelligence 126 (November 2023): 106717. http://dx.doi.org/10.1016/j.engappai.2023.106717.

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Poologaindran, Anujan, Mike Hart, Tom Santarius, et al. "Longitudinal Connectome Analyses following Low-Grade Glioma Neurosurgery: Implications for Cognitive Rehabilitation." Neuro-Oncology 23, Supplement_4 (2021): iv8. http://dx.doi.org/10.1093/neuonc/noab195.015.

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Abstract Aims Low-grade gliomas (LGG) slowly grow and infiltrate the brain's network architecture (the connectome). Unlike strokes that acutely damage the connectome, LGGs intricately remodel it, leading to varying deficits in executive function (i.e. attention, concentration, working memory). By longitudinally mapping the “mesoscale” architecture of the connectome, we may begin to systematically accelerate domain-general cognitive rehabilitation in LGG patients. In this study, we pursued the following aims: 1) track cognitive and connectome trajectories following LGG surgery, 2) determine opt

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Tang, Chang, Xinwang Liu, Xinzhong Zhu, et al. "CGD: Multi-View Clustering via Cross-View Graph Diffusion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5924–31. http://dx.doi.org/10.1609/aaai.v34i04.6052.

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Graph based multi-view clustering has been paid great attention by exploring the neighborhood relationship among data points from multiple views. Though achieving great success in various applications, we observe that most of previous methods learn a consensus graph by building certain data representation models, which at least bears the following drawbacks. First, their clustering performance highly depends on the data representation capability of the model. Second, solving these resultant optimization models usually results in high computational complexity. Third, there are often some hyper-

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Zhu, Fujian, and Shaojie Dai. "Multi-view Attention Mechanism Learning for POI Recommendation." Journal of Physics: Conference Series 2258, no. 1 (2022): 012041. http://dx.doi.org/10.1088/1742-6596/2258/1/012041.

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Abstract POI(point of interest) recommendation is a very necessary research field in both academic and commerce, however, predicting users’ potential points of interest is always faced with the problems of data sparsity and context semantics. Some studies have shown that graph embedding technology alleviates the problem of data sparsity to a certain extent. However, neither graph embedding techniques nor unsupervised learning models can adaptively learn the different effects of multiple relations between users and POIs, respectively. In view of this, we leverage the contextual information of u

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Zou, Yongqi, Wenjiang Feng, Juntao Zhang, and Jingfu Li. "Forecasting of Short-Term Load Using the MFF-SAM-GCN Model." Energies 15, no. 9 (2022): 3140. http://dx.doi.org/10.3390/en15093140.

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Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Netwo

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Zou, Yongqi, Wenjiang Feng, Juntao Zhang, and Jingfu Li. "Forecasting of Short-Term Load Using the MFF-SAM-GCN Model." Energies 15, no. 9 (2022): 3140. http://dx.doi.org/10.3390/en15093140.

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Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Netwo

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Wu, Fei, Changjiang Zheng, Chen Zhang, Junze Ma, and Kai Sun. "Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting." Applied Sciences 13, no. 2 (2023): 711. http://dx.doi.org/10.3390/app13020711.

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The key to intelligent traffic control and guidance lies in accurate prediction of traffic flow. Since traffic flow data is nonlinear, complex, and dynamic, in order to overcome these issues, graph neural network techniques are employed to address these challenges. For this reason, we propose a deep-learning architecture called AMGC-AT and apply it to a real passenger flow dataset of the Hangzhou metro for evaluation. Based on a priori knowledge, we set up multi-view graphs to express the static feature similarity of each station in the metro network, such as geographic location and zone funct

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Cui, Wanqiu, Junping Du, Dawei Wang, Feifei Kou, and Zhe Xue. "MVGAN: Multi-View Graph Attention Network for Social Event Detection." ACM Transactions on Intelligent Systems and Technology 12, no. 3 (2021): 1–24. http://dx.doi.org/10.1145/3447270.

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Social networks are critical sources for event detection thanks to the characteristics of publicity and dissemination. Unfortunately, the randomness and semantic sparsity of the social network text bring significant challenges to the event detection task. In addition to text, time is another vital element in reflecting events since events are often followed for a while. Therefore, in this article, we propose a novel method named Multi-View Graph Attention Network (MVGAN) for event detection in social networks. It enriches event semantics through both neighbor aggregation and multi-view fusion

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Huang, Zongmo, Yazhou Ren, Xiaorong Pu, Shudong Huang, Zenglin Xu, and Lifang He. "Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 7936–43. http://dx.doi.org/10.1609/aaai.v37i7.25960.

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As one of the most important research topics in the unsupervised learning field, Multi-View Clustering (MVC) has been widely studied in the past decade and numerous MVC methods have been developed. Among these methods, the recently emerged Graph Neural Networks (GNN) shine a light on modeling both topological structure and node attributes in the form of graphs, to guide unified embedding learning and clustering. However, the effectiveness of existing GNN-based MVC methods is still limited due to the insufficient consideration in utilizing the self-supervised information and graph information,

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Wang, Li, Xin Wang, and Jiao Wang. "Rail Transit Prediction Based on Multi-View Graph Attention Networks." Journal of Advanced Transportation 2022 (July 6, 2022): 1–8. http://dx.doi.org/10.1155/2022/4672617.

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Traffic prediction is the cornerstone of intelligent transportation system. In recent years, graph neural network has become the mainstream traffic prediction method due to its excellent processing ability of unstructured data. However, the network relationship in the real world is more complex. Multiple nodes and various associations such as different types of stations and lines in rail transit always exist at the same time. In an end-to-end model, the training accuracy will suffer if the same weights are assigned to multiple views. Thus, this paper proposes a framework with multi-view and mu

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Ling, Yawen, Jianpeng Chen, Yazhou Ren, et al. "Dual Label-Guided Graph Refinement for Multi-View Graph Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8791–98. http://dx.doi.org/10.1609/aaai.v37i7.26057.

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With the increase of multi-view graph data, multi-view graph clustering (MVGC) that can discover the hidden clusters without label supervision has attracted growing attention from researchers. Existing MVGC methods are often sensitive to the given graphs, especially influenced by the low quality graphs, i.e., they tend to be limited by the homophily assumption. However, the widespread real-world data hardly satisfy the homophily assumption. This gap limits the performance of existing MVGC methods on low homophilous graphs. To mitigate this limitation, our motivation is to extract high-level vi

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Lyu, Gengyu, Xiang Deng, Yanan Wu, and Songhe Feng. "Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7647–54. http://dx.doi.org/10.1609/aaai.v36i7.20731.

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In multi-view multi-label learning (MVML), each instance is described by several heterogeneous feature representations and associated with multiple valid labels simultaneously. Although diverse MVML methods have been proposed over the last decade, most previous studies focus on leveraging the shared subspace across different views to represent the multi-view consensus information, while it is still an open issue whether such shared subspace representation is necessary when formulating the desired MVML model. In this paper, we propose a DeepGCN based View-Specific MVML method (D-VSM) which can

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Zhang, Pei, Siwei Wang, Jingtao Hu, et al. "Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering." Sensors 20, no. 20 (2020): 5755. http://dx.doi.org/10.3390/s20205755.

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With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-view clustering methods hold on the assumption that all of the views are complete. However, in many real scenarios, multi-view data are often incomplete for many reasons, e.g., hardware failure or incomplete data collection. In this paper, we propose an adaptive weighted graph fusion incomplete multi-view subspace clustering (AWGF-IMSC) method to s

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Shang, Chao, Qinqing Liu, Qianqian Tong, Jiangwen Sun, Minghu Song, and Jinbo Bi. "Multi-view spectral graph convolution with consistent edge attention for molecular modeling." Neurocomputing 445 (July 2021): 12–25. http://dx.doi.org/10.1016/j.neucom.2021.02.025.

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Yu, Jinshi, Qi Duan, Haonan Huang, Shude He, and Tao Zou. "Effective Incomplete Multi-View Clustering via Low-Rank Graph Tensor Completion." Mathematics 11, no. 3 (2023): 652. http://dx.doi.org/10.3390/math11030652.

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In the past decade, multi-view clustering has received a lot of attention due to the popularity of multi-view data. However, not all samples can be observed from every view due to some unavoidable factors, resulting in the incomplete multi-view clustering (IMC) problem. Up until now, most efforts for the IMC problem have been made on the learning of consensus representations or graphs, while many missing views are ignored, making it impossible to capture the information hidden in the missing view. To overcome this drawback, we first analyzed the low-rank relationship existing inside each graph

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Huang, Yanquan, Haoliang Yuan, and Loi Lei Lai. "Latent multi-view semi-supervised classification by using graph learning." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 05 (2020): 2050039. http://dx.doi.org/10.1142/s0219691320500393.

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Multi-view learning is a hot research direction in the field of machine learning and pattern recognition, which is attracting more and more attention recently. In the real world, the available data commonly include a small number of labeled samples and a large number of unlabeled samples. In this paper, we propose a latent multi-view semi-supervised classification method by using graph learning. This work recovers a latent intact representation to utilize the complementary information of the multi-view data. In addition, an adaptive graph learning technique is adopted to explore the local stru

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Alothali, Eiman, Motamen Salih, Kadhim Hayawi, and Hany Alashwal. "Bot-MGAT: A Transfer Learning Model Based on a Multi-View Graph Attention Network to Detect Social Bots." Applied Sciences 12, no. 16 (2022): 8117. http://dx.doi.org/10.3390/app12168117.

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Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approaches to solving this problem. Due to the scarcity of recently updated labeled data, the performance of detection systems degrades when exposed to a new dataset. Therefore, semi-supervised learning (SSL) techniques can improve performance, using both labeled and unlabeled examples. In this paper, we propose a framework based on the multi-view graph

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Zeng, Hui, Tianmeng Zhao, Ruting Cheng, Fuzhou Wang, and Jiwei Liu. "Hierarchical Graph Attention Based Multi-View Convolutional Neural Network for 3D Object Recognition." IEEE Access 9 (2021): 33323–35. http://dx.doi.org/10.1109/access.2021.3059853.

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Zhu, Jiangqiang, Kai Li, Jinjia Peng, and Jing Qi. "Self-Supervised Graph Attention Collaborative Filtering for Recommendation." Electronics 12, no. 4 (2023): 793. http://dx.doi.org/10.3390/electronics12040793.

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Due to the complementary nature of graph neural networks and structured data in recommendations, recommendation systems using graph neural network techniques have become mainstream. However, there are still problems, such as sparse supervised signals and interaction noise, in the recommendation task. Therefore, this paper proposes a self-supervised graph attention collaborative filtering for recommendation (SGACF). The correlation between adjacent nodes is deeply mined using a multi-head graph attention network to obtain accurate node representations. It is worth noting that self-supervised le

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