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1
Hao, Jia, Yonghong Tian, Qingqing Zhang, and Genmao Zhang. "Mongolian-Chinese Machine Translation Based on Graph Neural Network." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012050. http://dx.doi.org/10.1088/1742-6596/2400/1/012050.
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Abstract Most of the current Mongolian-Chinese neural machine translation relies on a sequence-to-sequence-based encoder-decoder, which does not effectively utilize the syntactic information of sentences as well as the hierarchical information of the language. To solve this problem, a graph-to-sequence-based encoder-decoder is constructed by introducing graph neural networks, making better use of syntactic information and semantic knowledge of sentences. We will build a Mongolian dependency tree library and design a densely connected graph convolutional neural network (D-GCN) based on GCN combined with a densely connected network (DenseNet). Experiments are conducted to compare with the sequence-to-sequence based Mongolian-Chinese neural machine translation model and the variant model of graph neural network, and the results verify the advantages of the D-GCN-based Mongolian-Chinese neural machine translation model in translation performance.
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Scarselli, F., M. Gori, Ah Chung Tsoi, M. Hagenbuchner, and G. Monfardini. "The Graph Neural Network Model." IEEE Transactions on Neural Networks 20, no. 1 (January 2009): 61–80. http://dx.doi.org/10.1109/tnn.2008.2005605.
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Elnaggar, Sarah G., Ibrahim E. Elsemman, and Taysir Hassan A. Soliman. "Embedding-Based Deep Neural Network and Convolutional Neural Network Graph Classifiers." Electronics 12, no. 12 (June 17, 2023): 2715. http://dx.doi.org/10.3390/electronics12122715.
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One of the most significant graph data analysis tasks is graph classification, as graphs are complex data structures used for illustrating relationships between entity pairs. Graphs are essential in many domains, such as the description of chemical molecules, biological networks, social relationships, etc. Real-world graphs are complicated and large. As a result, there is a need to find a way to represent or encode a graph’s structure so that it can be easily utilized by machine learning models. Therefore, graph embedding is considered one of the most powerful solutions for graph representation. Inspired by the Doc2Vec model in Natural Language Processing (NLP), this paper first investigates different ways of (sub)graph embedding to represent each graph or subgraph as a fixed-length feature vector, which is then used as input to any classifier. Thus, two supervised classifiers—a deep neural network (DNN) and a convolutional neural network (CNN)—are proposed to enhance graph classification. Experimental results on five benchmark datasets indicate that the proposed models obtain competitive results and are superior to some traditional classification methods and deep-learning-based approaches on three out of five benchmark datasets, with an impressive accuracy rate of 94% on the NCI1 dataset.
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Bi, Zhongqin, Lina Jing, Meijing Shan, Shuming Dou, and Shiyang Wang. "Hierarchical Social Recommendation Model Based on a Graph Neural Network." Wireless Communications and Mobile Computing 2021 (August 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/9107718.
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With the continuous accumulation of social network data, social recommendation has become a widely used recommendation method. Based on the theory of social relationship propagation, mining user relationships in social networks can alleviate the problems of data sparsity and the cold start of recommendation systems. Therefore, integrating social information into recommendation systems is of profound importance. We present an efficient network model for social recommendation. The model is based on the graph neural network. It unifies the attention mechanism and bidirectional LSTM into the same framework and uses a multilayer perceptron. In addition, an embedded propagation method is added to learn the neighbor influences of different depths and extract useful neighbor information for social relationship modeling. We use this method to solve the problem that the current research methods of social recommendation only extract the superficial level of social networks but ignore the importance of the relationship strength of the users at different levels in the recommendation. This model integrates social relationships into user and project interactions, not only capturing the weight of the relationship between different users but also considering the influence of neighbors at different levels on user preferences. Experiments on two public datasets demonstrate that the proposed model is superior to other benchmark methods with respect to mean absolute error and root mean square error and can effectively improve the quality of recommendations.
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AlBadani, Barakat, Ronghua Shi, Jian Dong, Raeed Al-Sabri, and Oloulade Babatounde Moctard. "Transformer-Based Graph Convolutional Network for Sentiment Analysis." Applied Sciences 12, no. 3 (January 26, 2022): 1316. http://dx.doi.org/10.3390/app12031316.
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Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years. Recently, deep neural network (DNN) models have been used for sentiment analysis tasks, achieving promising results. Although these models can analyze sequences of arbitrary length, utilizing them in the feature extraction layer of a DNN increases the dimensionality of the feature space. More recently, graph neural networks (GNNs) have achieved a promising performance in different NLP tasks. However, previous models cannot be transferred to a large corpus and neglect the heterogeneity of textual graphs. To overcome these difficulties, we propose a new Transformer-based graph convolutional network for heterogeneous graphs called Sentiment Transformer Graph Convolutional Network (ST-GCN). To the best of our knowledge, this is the first study to model the sentiment corpus as a heterogeneous graph and learn document and word embeddings using the proposed sentiment graph transformer neural network. In addition, our model offers an easy mechanism to fuse node positional information for graph datasets using Laplacian eigenvectors. Extensive experiments on four standard datasets show that our model outperforms the existing state-of-the-art models.
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Li, Boao. "Multi-modal sentiment analysis based on graph neural network." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 792–98. http://dx.doi.org/10.54254/2755-2721/6/20230918.
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Thanks to popularity of social media, people are witnessing the rapid proliferation of posts with various modalities. It is worth noting that these multi-modal expressions share certain characteristics, including the interdependence of objects in the posted images, which is sometimes overlooked in previous researches as they focused on single image-text posts and pay little attention on obtaining the global features. In this paper, a neural network with multiple channels for image-text sentiment detection is proposed. The first step is to encode text and images to capture implicit tendencies. Then the introduction of this model obtains multi-modal expressions by collecting the shared characteristics of the dataset. Finally, the attention mechanism provides reliable predictions of the sentiment tendencies of the given pairs of image-text data. The results of experiments conducted on two publicly available datasets crawled from Twitter prove the reliability of the model on multi-modal sentiment detection, since the model precedes previously proposed models in the main evaluating criteria.
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Xu, Jinling, Yanping Chen, Yongbin Qin, Ruizhang Huang, and Qinghua Zheng. "A Feature Combination-Based Graph Convolutional Neural Network Model for Relation Extraction." Symmetry 13, no. 8 (August 9, 2021): 1458. http://dx.doi.org/10.3390/sym13081458.
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The task to extract relations tries to identify relationships between two named entities in a sentence. Because a sentence usually contains several named entities, capturing structural information of a sentence is important to support this task. Currently, graph neural networks are widely implemented to support relation extraction, in which dependency trees are employed to generate adjacent matrices for encoding structural information of a sentence. Because parsing a sentence is error-prone, it influences the performance of a graph neural network. On the other hand, a sentence is structuralized by several named entities, which precisely segment a sentence into several parts. Different features can be combined by prior knowledge and experience, which are effective to initialize a symmetric adjacent matrix for a graph neural network. Based on this phenomenon, we proposed a feature combination-based graph convolutional neural network model (FC-GCN). It has the advantages of encoding structural information of a sentence, considering prior knowledge, and avoiding errors caused by parsing. In the experiments, the results show significant improvement, which outperform existing state-of-the-art performances.
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Gao, Huiguo, Mengyuan Lee, Guanding Yu, and Zhaolin Zhou. "A Graph Neural Network Based Decentralized Learning Scheme." Sensors 22, no. 3 (January 28, 2022): 1030. http://dx.doi.org/10.3390/s22031030.
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As an emerging paradigm considering data privacy and transmission efficiency, decentralized learning aims to acquire a global model using the training data distributed over many user devices. It is a challenging problem since link loss, partial device participation, and non-independent and identically distributed (non-iid) data distribution would all deteriorate the performance of decentralized learning algorithms. Existing work may restrict to linear models or show poor performance over non-iid data. Therefore, in this paper, we propose a decentralized learning scheme based on distributed parallel stochastic gradient descent (DPSGD) and graph neural network (GNN) to deal with the above challenges. Specifically, each user device participating in the learning task utilizes local training data to compute local stochastic gradients and updates its own local model. Then, each device utilizes the GNN model and exchanges the model parameters with its neighbors to reach the average of resultant global models. The iteration repeats until the algorithm converges. Extensive simulation results over both iid and non-iid data validate the algorithm’s convergence to near optimal results and robustness to both link loss and partial device participation.
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Zhong, Hongwei, Mingyang Wang, and Xinyue Zhang. "HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network." Electronics 12, no. 9 (May 6, 2023): 2124. http://dx.doi.org/10.3390/electronics12092124.
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Network embedding is an effective way to realize the quantitative analysis of large-scale networks. However, mainstream network embedding models are limited by the manually pre-set metapaths, which leads to the unstable performance of the model. At the same time, the information from homogeneous neighbors is mostly focused in encoding the target node, while ignoring the role of heterogeneous neighbors in the node embedding. This paper proposes a new embedding model, HeMGNN, for heterogeneous networks. The framework of the HeMGNN model is divided into two modules: the metapath subgraph extraction module and the node embedding mixing module. In the metapath subgraph extraction module, HeMGNN automatically generates and filters out the metapaths related to domain mining tasks, so as to effectively avoid the excessive dependence of network embedding on artificial prior knowledge. In the node embedding mixing module, HeMGNN integrates the information of homogeneous and heterogeneous neighbors when learning the embedding of the target nodes. This makes the node vectors generated according to the HeMGNN model contain more abundant topological and semantic information provided by the heterogeneous networks. The Rich semantic information makes the node vectors achieve good performance in downstream domain mining tasks. The experimental results show that, compared to the baseline models, the average classification and clustering performance of HeMGNN has improved by up to 0.3141 and 0.2235, respectively.
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Zhang, Guoxing, Haixiao Wang, and Yuanpu Yin. "Multi-type Parameter Prediction of Traffic Flow Based on Time-space Attention Graph Convolutional Network." International Journal of Circuits, Systems and Signal Processing 15 (August 11, 2021): 902–12. http://dx.doi.org/10.46300/9106.2021.15.97.
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Graph Convolutional Neural Networks are more and more widely used in traffic flow parameter prediction tasks by virtue of their excellent non-Euclidean spatial feature extraction capabilities. However, most graph convolutional neural networks are only used to predict one type of traffic flow parameter. This means that the proposed graph convolutional neural network may only be effective for specific parameters of specific travel modes. In order to improve the universality of graph convolutional neural networks. By embedding time feature and spatio-temporal attention layer, we propose a spatio-temporal attention graph convolutional neural network based on the attention mechanism of the neural network. Through experiments on passenger flow data and vehicle speed data of two different travel modes (Hangzhou Metro Data and California Highway Data), it is verified that the proposed spatio-temporal attention graph convolutional neural network can be used to predict passenger flow and vehicle speed simultaneously. Meanwhile, the error distribution range of the proposed model is minimum, and the overall level of prediction results is more accurate.
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Irani Hoeronis and Bambang Riyanto Trilaksono. "Node Classification on The Citation Network Using Graph Neural Network." Inspiration: Jurnal Teknologi Informasi dan Komunikasi 13, no. 1 (June 30, 2023): 96–105. http://dx.doi.org/10.35585/inspir.v13i1.49.
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Research on Graph Neural Networks has influenced various current real-world problems. The graph-based approach is considered capable of effectively representing the actual state of surrounding data by utilizing nodes, edges, and features. Consider the feedforward neural network and the graph neural network approaches, we determine the accuracy of each method. In the baseline experiment, training and testing were performed using the NN approach. The resulting accuracy of FNN was 72.59% and GNN model has increased by 81.65%. There is a 9.06% increase in accuracy between the baseline model and the GNN model. The new data utilized in the model predictions showcases the probabilities of each class through randomly generated examples.
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Buchhorn, Katie, Edgar Santos-Fernandez, Kerrie Mengersen, and Robert Salomone. "Graph neural network-based anomaly detection for river network systems." F1000Research 12 (August 16, 2023): 991. http://dx.doi.org/10.12688/f1000research.136097.1.
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Background: Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology. Anomaly detection is crucial for identifying erroneous patterns in sensor data, but can be a challenging task due to the complexity and variability of the data, even under typical conditions. This paper presents a solution to the challenging task of anomaly detection for river network sensor data, which is essential for accurate and continuous monitoring. Methods: We use a graph neural network model, the recently proposed Graph Deviation Network (GDN), which employs graph attention-based forecasting to capture the complex spatio-temporal relationships between sensors. We propose an alternate anomaly threshold criteria for the model, GDN+, based on the learned graph. To evaluate the model's efficacy, we introduce new benchmarking simulation experiments with highly-sophisticated dependency structures and subsequence anomalies of various types. We also introduce software called gnnad. Results: We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data. Conclusions: Findings suggest that GDN+ outperforms the baseline approach in high-dimensional data, while also providing improved interpretability.
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Serafini, Marco. "Scalable Graph Neural Network Training." ACM SIGOPS Operating Systems Review 55, no. 1 (June 2, 2021): 68–76. http://dx.doi.org/10.1145/3469379.3469387.
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Graph Neural Networks (GNNs) are a new and increasingly popular family of deep neural network architectures to perform learning on graphs. Training them efficiently is challenging due to the irregular nature of graph data. The problem becomes even more challenging when scaling to large graphs that exceed the capacity of single devices. Standard approaches to distributed DNN training, like data and model parallelism, do not directly apply to GNNs. Instead, two different approaches have emerged in the literature: whole-graph and sample-based training. In this paper, we review and compare the two approaches. Scalability is challenging with both approaches, but we make a case that research should focus on sample-based training since it is a more promising approach. Finally, we review recent systems supporting sample-based training.
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Cui, Mengtian, Songlin Long, Yue Jiang, and Xu Na. "Research of Software Defect Prediction Model Based on Complex Network and Graph Neural Network." Entropy 24, no. 10 (September 27, 2022): 1373. http://dx.doi.org/10.3390/e24101373.
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The goal of software defect prediction is to make predictions by mining the historical data using models. Current software defect prediction models mainly focus on the code features of software modules. However, they ignore the connection between software modules. This paper proposed a software defect prediction framework based on graph neural network from a complex network perspective. Firstly, we consider the software as a graph, where nodes represent the classes, and edges represent the dependencies between the classes. Then, we divide the graph into multiple subgraphs using the community detection algorithm. Thirdly, the representation vectors of the nodes are learned through the improved graph neural network model. Lastly, we use the representation vector of node to classify the software defects. The proposed model is tested on the PROMISE dataset, using two graph convolution methods, based on the spectral domain and spatial domain in the graph neural network. The investigation indicated that both convolution methods showed an improvement in various metrics, such as accuracy, F-measure, and MCC (Matthews correlation coefficient) by 86.6%, 85.8%, and 73.5%, and 87.5%, 85.9%, and 75.5%, respectively. The average improvement of various metrics was noted as 9.0%, 10.5%, and 17.5%, and 6.3%, 7.0%, and 12.1%, respectively, compared with the benchmark models.
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Zhu, Zhiying, Hang Zhou, Siyuan Xing, Zhenxing Qian, Sheng Li, and Xinpeng Zhang. "Perceptual Hash of Neural Networks." Symmetry 14, no. 4 (April 13, 2022): 810. http://dx.doi.org/10.3390/sym14040810.
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In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models.
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Zhu, Hongqiang. "A graph neural network-enhanced knowledge graph framework for intelligent analysis of policing cases." Mathematical Biosciences and Engineering 20, no. 7 (2023): 11585–604. http://dx.doi.org/10.3934/mbe.2023514.
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<abstract> <p>In this paper, we model a knowledge graph based on graph neural networks, conduct an in-depth study on building knowledge graph embeddings for policing cases, and design a graph neural network-enhanced knowledge graph framework. In detail, we use the label propagation algorithm (LPA) to assist the convolutional graph network (GCN) in training the edge weights of the knowledge graph to construct a policing case prediction method. This improves the traditional convolutional neural network from a single-channel network to a multichannel network to accommodate the multiple feature factors of policing cases. In addition, this expands the perceptual field of the convolutional neural network to improve prediction accuracy. The experimental results show that the multichannel convolutional neural network's prediction accuracy can reach 87.7%. To ensure the efficiency of the security case analysis network, an efficient pairwise feature extraction base module is added to enhance the backbone network, which reduces the number of parameters of the whole network and decreases the complexity of operations. We experimentally demonstrate that this method achieves a better balance of efficiency and performance by obtaining approximate results with 53.5% fewer floating-point operations and 70.2% fewer number parameters than its contemporary work.</p> </abstract>
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Son, Jeongtae, and Dongsup Kim. "Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities." PLOS ONE 16, no. 4 (April 8, 2021): e0249404. http://dx.doi.org/10.1371/journal.pone.0249404.
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Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding affinity. Graph convolutional neural networks reduce the computational time and resources that are normally required by the traditional convolutional neural network models. In this technique, the structure of a protein-ligand complex is represented as a graph of multiple adjacency matrices whose entries are affected by distances, and a feature matrix that describes the molecular properties of the atoms. We evaluated the predictive power of GraphBAR for protein-ligand binding affinities by using PDBbind datasets and proved the efficiency of the graph convolution. Given the computational efficiency of graph convolutional neural networks, we also performed data augmentation to improve the model performance. We found that data augmentation with docking simulation data could improve the prediction accuracy although the improvement seems not to be significant. The high prediction performance and speed of GraphBAR suggest that such networks can serve as valuable tools in drug discovery.
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Zhao, Chensu, Yang Xin, Xuefeng Li, Hongliang Zhu, Yixian Yang, and Yuling Chen. "An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks." Applied Sciences 10, no. 22 (November 18, 2020): 8160. http://dx.doi.org/10.3390/app10228160.
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With the rapid development of social networks, spam bots and other anomaly accounts’ malicious behavior has become a critical information security problem threatening the social network platform. In order to reduce this threat, the existing research mainly uses feature-based detection or propagation-based detection, and it applies machine learning or graph mining algorithms to identify anomaly accounts in social networks. However, with the development of technology, spam bots are becoming more advanced, and identifying bots is still an open challenge. This paper proposes a new semi-supervised graph embedding model based on a graph attention network for spam bot detection in social networks. This approach constructs a detection model by aggregating features and neighbor relationships, and learns a complex method to integrate the different neighborhood relationships between nodes to operate the directed social graph. The new model can identify spam bots by capturing user features and two different relationships among users in social networks. We compare our method with other methods on real-world social network datasets, and the experimental results show that our proposed model achieves a significant and consistent improvement.
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Nan, Mihai, and Adina Magda Florea. "Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition." Sensors 22, no. 19 (September 20, 2022): 7117. http://dx.doi.org/10.3390/s22197117.
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Human action recognition has a wide range of applications, including Ambient Intelligence systems and user assistance. Starting from the recognized actions performed by the user, a better human–computer interaction can be achieved, and improved assistance can be provided by social robots in real-time scenarios. In this context, the performance of the prediction system is a key aspect. The purpose of this paper is to introduce a neural network approach based on various types of convolutional layers that can achieve a good performance in recognizing actions but with a high inference speed. The experimental results show that our solution, based on a combination of graph convolutional networks (GCN) and temporal convolutional networks (TCN), is a suitable approach that reaches the proposed goal. In addition to the neural network model, we design a pipeline that contains two stages for obtaining relevant geometric features, data augmentation and data preprocessing, also contributing to an increased performance.
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Yang, Shuai, Yueqin Zhang, and Zehua Zhang. "Runoff Prediction Based on Dynamic Spatiotemporal Graph Neural Network." Water 15, no. 13 (July 5, 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 spatiotemporal features. The temporal features are captured by the long short-term memory network (LSTM) with the self-attention mechanism. Then, the upstream and downstream distance matrices are constructed based on the river network topology in the basin, the dynamic matrix is constructed based on the runoff sequence, and the spatial dependence is captured by combining the above two matrices through the diffusion process. After that, the residual sequences are input to the next layer by the decoupling block, and, finally, the prediction results are output after multi-layer stacking. Experiments are conducted on the historical runoff dataset in the Upper Delaware River Basin, and the MAE, MSE, MAPE, and NSE were the best compared with the baseline model for forecasting periods of 3 h, 6 h, and 9 h. The experimental results show that DSTGNN can better capture the spatiotemporal characteristics and has higher prediction accuracy.
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Liu, Qiang, Wei Zhu, Feng Ma, Xiyu Jia, Yu Gao, and Jun Wen. "Graph attention network-based fluid simulation model." AIP Advances 12, no. 9 (September 1, 2022): 095114. http://dx.doi.org/10.1063/5.0122165.
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Traditional computational fluid dynamics (CFD) techniques deduce the dynamic variations in flow fields by using finite elements or finite differences to solve partial differential equations. CFD usually involves several tens of thousands of grid nodes, which entail long computation times and significant computational resources. Fluid data are usually irregular data, and there will be turbulence in the flow field where the physical quantities between adjacent grid nodes are extremely nonequilibrium. We use a graph attention neural network to build a fluid simulation model (GAFM). GAFM assigns weights to adjacent node-pairs through a graph attention mechanism. In this way, it is not only possible to directly calculate the fluid data but also to adjust for nonequilibrium in vortices, especially turbulent flows. The GAFM deductively predicts the dynamic variations in flow fields by using spatiotemporally continuous sample data. A validation of the proposed GAFM against the two-dimensional (2D) flow around a cylinder confirms its high prediction accuracy. In addition, the GAFM achieves faster computation speeds than traditional CFD solvers by two to three orders of magnitude. The GAFM provides a new idea for the rapid optimization and design of fluid mechanics models and the real-time control of intelligent fluid mechanisms.
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Zhu, Li, Xin Pan, Xinpeng Wang, and Fu Haito. "A Small Sample Recognition Model for Poisonous and Edible Mushrooms based on Graph Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (August 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/2276318.
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The automatic identification of disease types of edible mushroom crops and poisonous crops is of great significance for improving crop yield and quality. Based on the graph convolutional neural network theory, this paper constructs a graph convolutional network model for the identification of poisonous crops and edible fungi. By constructing 6 graph convolutional networks with different depths, the model uses the training mechanism of graph convolutional networks to analyze the results of disease identification and completes the automatic extraction of the disease characteristics of the poisonous crops by overfitting problem. During the simulation, firstly, the relevant PlantVillage dataset is used to obtain the pretrained model, and the parameters are adjusted to fit the dataset. The network framework is trained and parameterized with prior knowledge learned from large datasets and finally synthesized by training multiple neural network models and using direct averaging and weighting to synthesize their predictions. The experimental results show that the graph convolutional neural network model that integrates multi-scale category relationships and dense links can use dense connection technology to improve the representation ability and generalization ability of the model, and the accuracy rate generally increases by 1%–10%. The average recognition rate is about 91%, which greatly promotes the ability to identify the diseases of poisonous crops.
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Lin, Lei, Weizi Li, and Lei Zhu. "Data-Driven Graph Filter-Based Graph Convolutional Neural Network Approach for Network-Level Multi-Step Traffic Prediction." Sustainability 14, no. 24 (December 13, 2022): 16701. http://dx.doi.org/10.3390/su142416701.
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Accurately predicting network-level traffic conditions has been identified as a critical need for smart and advanced transportation services. In recent decades, machine learning and artificial intelligence have been widely applied for traffic state, including traffic volume prediction. This paper proposes a novel deep learning model, Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF), for network-wide multi-step traffic volume prediction. More specifically, the proposed GCNN-DDGF model can automatically capture hidden spatiotemporal correlations between traffic detectors, and its sequence-to-sequence recurrent neural network architecture is able to further utilize temporal dependency from historical traffic flow data for multi-step prediction. The proposed model was tested in a network-wide hourly traffic volume dataset between 1 January 2018 and 30 June 2019 from 150 sensors in the Los Angeles area. Detailed experimental results illustrate that the proposed model outperforms the other five widely used deep learning and machine learning models in terms of computational efficiency and prediction accuracy. For instance, the GCNN-DDGF model improves MAE, MAPE, and RMSE by 25.33%, 20.45%, and 29.20% compared to the state-of-the-art models, such as Diffusion Convolution Recurrent Neural Network (DCRNN), which is widely accepted as a popular and effective deep learning model.
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Wu, Wei, Guangmin Hu, and Fucai Yu. "Ricci Curvature-Based Semi-Supervised Learning on an Attributed Network." Entropy 23, no. 3 (February 27, 2021): 292. http://dx.doi.org/10.3390/e23030292.
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In recent years, on the basis of drawing lessons from traditional neural network models, people have been paying more and more attention to the design of neural network architectures for processing graph structure data, which are called graph neural networks (GNN). GCN, namely, graph convolution networks, are neural network models in GNN. GCN extends the convolution operation from traditional data (such as images) to graph data, and it is essentially a feature extractor, which aggregates the features of neighborhood nodes into those of target nodes. In the process of aggregating features, GCN uses the Laplacian matrix to assign different importance to the nodes in the neighborhood of the target nodes. Since graph-structured data are inherently non-Euclidean, we seek to use a non-Euclidean mathematical tool, namely, Riemannian geometry, to analyze graphs (networks). In this paper, we present a novel model for semi-supervised learning called the Ricci curvature-based graph convolutional neural network, i.e., RCGCN. The aggregation pattern of RCGCN is inspired by that of GCN. We regard the network as a discrete manifold, and then use Ricci curvature to assign different importance to the nodes within the neighborhood of the target nodes. Ricci curvature is related to the optimal transport distance, which can well reflect the geometric structure of the underlying space of the network. The node importance given by Ricci curvature can better reflect the relationships between the target node and the nodes in the neighborhood. The proposed model scales linearly with the number of edges in the network. Experiments demonstrated that RCGCN achieves a significant performance gain over baseline methods on benchmark datasets.
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Shi, Ming, Jing Zhao, and Donglin Wu. "Convolutional Neural Network Knowledge Graph Link Prediction Model Based on Relational Memory." Computational Intelligence and Neuroscience 2023 (January 31, 2023): 1–9. http://dx.doi.org/10.1155/2023/3909697.
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A knowledge graph is a collection of fact triples, a semantic network composed of nodes and edges. Link prediction from knowledge graphs is used to reason about missing parts of triples. Common knowledge graph link prediction models include translation models, semantics matching models, and neural network models. However, the translation models and semantic matching models have relatively simple structures and poor expressiveness. The neural network model can easily ignore the overall structural characteristics of triples and cannot capture the links between entities and relations in low-dimensional space. In response to the above problems, we propose a knowledge graph embedding model based on a relational memory network and convolutional neural network (RMCNN). We encode triple embedding vectors using a relational memory network and decode using a convolutional neural network. First, we will obtain entity and relation vectors by encoding the latent dependencies between entities and relations and some critical information and keeping the translation properties of triples. Then, we compose a matrix of head entity encoding embedding vector, relation encoding embedding vector, and tail entity embedding encoding vector as the input of the convolutional neural network. Finally, we use a convolutional neural network as the decoder and a dimension conversion strategy to improve the information interaction capability of entities and relations in more dimensions. Experiments show that our model achieves significant progress and outperforms existing models and methods on several metrics.
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Li, Wei, Shaogang Gong, and Xiatian Zhu. "Neural Graph Embedding for Neural Architecture Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4707–14. http://dx.doi.org/10.1609/aaai.v34i04.5903.
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Existing neural architecture search (NAS) methods often operate in discrete or continuous spaces directly, which ignores the graphical topology knowledge of neural networks. This leads to suboptimal search performance and efficiency, given the factor that neural networks are essentially directed acyclic graphs (DAG). In this work, we address this limitation by introducing a novel idea of neural graph embedding (NGE). Specifically, we represent the building block (i.e. the cell) of neural networks with a neural DAG, and learn it by leveraging a Graph Convolutional Network to propagate and model the intrinsic topology information of network architectures. This results in a generic neural network representation integrable with different existing NAS frameworks. Extensive experiments show the superiority of NGE over the state-of-the-art methods on image classification and semantic segmentation.
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Huang, Zhenhua, Yinhao Tang, and Yunwen Chen. "A graph neural network-based node classification model on class-imbalanced graph data." Knowledge-Based Systems 244 (May 2022): 108538. http://dx.doi.org/10.1016/j.knosys.2022.108538.
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Gan, Ling, and Peng He. "Single Document Extractive Summarization Model Based on Heterogeneous Graph Transformer." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2171/1/012012.
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Abstract At present, the graph model-based summary model has problems such as insufficient semantic fusion between nodes and lack of location information. Therefore, this paper proposes a single-document extraction text summary model based on a heterogeneous graph attention neural network, using HGT (Heterogeneous Graph Transformer), Heterogeneous Graph Attention Neural Network to solve the defect of insufficient deep semantic fusion of nodes, and use trainable position coding to solve the defect of missing position information. Experiments show that the model in this paper has improved on the three evaluation indicators of R_1, R_2 and R_L, and the abstracts extracted have better generality.
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Chen, Yu-Hung, Jiann-Liang Chen, and Ren-Feng Deng. "Similarity-Based Malware Classification Using Graph Neural Networks." Applied Sciences 12, no. 21 (October 26, 2022): 10837. http://dx.doi.org/10.3390/app122110837.
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This work proposes a novel malware identification model that is based on a graph neural network (GNN). The function call relationship and function assembly content obtained by analyzing the malware are used to generate a graph that represents the functional structure of a malware sample. In addition to establishing a multi-classification model for predicting malware family, this work implements a similarity model that is based on Siamese networks, measuring the distance between two samples in the feature space to determine whether they belong to the same malware family. The distance between the samples is gradually adjusted during the training of the model to improve the performance. A Malware Bazaar dataset analysis reveals that the proposed classification model has an accuracy and area under the curve (AUC) of 0.934 and 0.997, respectively. The proposed similarity model has an accuracy and AUC of 0.92 and 0.92, respectively. Further, the proposed similarity model identifies the unseen malware family with approximately 70% accuracy. Hence, the proposed similarity model exhibits better performance and scalability than the pure classification model and previous studies.
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Han, Xiao, Chunhong Zhang, Yang Ji, and Zheng Hu. "A Dilated Recurrent Neural Network-Based Model for Graph Embedding." IEEE Access 7 (2019): 32085–92. http://dx.doi.org/10.1109/access.2019.2901804.
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Wang, Bin, Yu Chen, Jinfang Sheng, and Zhengkun He. "Attributed Graph Embedding Based on Attention with Cluster." Mathematics 10, no. 23 (December 1, 2022): 4563. http://dx.doi.org/10.3390/math10234563.
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Graph embedding is of great significance for the research and analysis of graphs. Graph embedding aims to map nodes in the network to low-dimensional vectors while preserving information in the original graph of nodes. In recent years, the appearance of graph neural networks has significantly improved the accuracy of graph embedding. However, the influence of clusters was not considered in existing graph neural network (GNN)-based methods, so this paper proposes a new method to incorporate the influence of clusters into the generation of graph embedding. We use the attention mechanism to pass the message of the cluster pooled result and integrate the whole process into the graph autoencoder as the third layer of the encoder. The experimental results show that our model has made great improvement over the baseline methods in the node clustering and link prediction tasks, demonstrating that the embeddings generated by our model have excellent expressiveness.
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Jing, Weipeng, Xianyang Song, Donglin Di, and Houbing Song. "geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classification." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 5 (September 30, 2021): 1–18. http://dx.doi.org/10.1145/3434239.
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In the area of geographic information processing, there are few researches on geographic text classification. However, the application of this task in Chinese is relatively rare. In our work, we intend to implement a method to extract text containing geographical entities from a large number of network texts. The geographic information in these texts is of great practical significance to transportation, urban and rural planning, disaster relief, and other fields. We use the method of graph convolutional neural network with attention mechanism to achieve this function. Graph attention networks (GAT) is an improvement of graph convolutional neural networks (GCN). Compared with GCN, the advantage of GAT is that the attention mechanism is proposed to weight the sum of the characteristics of adjacent vertices. In addition, We construct a Chinese dataset containing geographical classification from multiple datasets of Chinese text classification. The Macro-F Score of the geoGAT we used reached 95% on the new Chinese dataset.
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Liao, Guoqiong, and Xiaobin Deng. "Leveraging Social Relationship-Based Graph Attention Model for Group Event Recommendation." Wireless Communications and Mobile Computing 2020 (October 29, 2020): 1–14. http://dx.doi.org/10.1155/2020/8834450.
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Recently, event-based social networks(EBSN) such as Meetup, Plancast, and Douban have become popular. As users in the networks usually take groups as an unit to participate in events, it is necessary and meaningful to study effective strategies for recommending events to groups. Existing research on group event recommendation either has the problems of data sparse and cold start due to without considering of social relationships in the networks or makes the assumption that the influence weights between any pair of nodes in the user social graph are equal. In this paper, inspired by the graph neural network and attention mechanism, we propose a novel recommendation model named leveraging social relationship-based graph attention model (SRGAM) for group event recommendation. Specifically, we not only construct a user-event interaction graph and an event-user interaction graph, but also build a user-user social graph and an event-event social graph, to alleviate the problems of data sparse and cold start. In addition, by using a graph attention neural network to learn graph data, we can calculate the influence weight of each node in the graph, thereby generating more reasonable user latent vectors and event latent vectors. Furthermore, we use an attention mechanism to fuse multiple user vectors in a group, so as to generate a high-level group latent vector for rating prediction. Extensive experiments on real-world Meetup datasets demonstrate the effectiveness of the proposed model.
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Li, Guoyan, Yihui Shang, Yi Liu, and Xiangru Zhou. "A Network Traffic Prediction Model Based on Graph Neural Network in Software-Defined Networking." International Journal of Information Security and Privacy 16, no. 1 (January 1, 2022): 1–17. http://dx.doi.org/10.4018/ijisp.309130.
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The software-defined network (SDN) is a new network architecture system that achieves the separation of the data plane and the control plane, making SDN networks more relevant to research. Real-time accurate network traffic prediction plays a crucial role in SDN networks, and the spatio-temporal correlation and autocorrelation of SDN make traditional methods unable to meet the requirements of the prediction tasks. In this article, a SDN network traffic prediction model DI-GCN (deep information-GCN) is proposed, which firstly fuses graph convolution with gated convolutional units; secondly, the matrix of mutual information relation is defined and constructed to obtain the relational weight representation of traffic data. The proposed model was compared with GCN, GRU, and T-GCN on the real dataset GÉANT, respectively. Experiments show that the DI-GCN model not only ensures the ability to represent the actual data but also reduces the prediction error as well as achieved better prediction results.
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Zhang, Yinan, and Wenyu Chen. "Incorporating Siamese Network Structure into Graph Neural Network." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2171/1/012023.
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Abstract Siamese network plays an important role in many artificial intelligence domains, but there requires more exploration of applying Siamese structure to graph neural network. This paper proposes a novel framework that incorporates Siamese network structure into Graph Neural Network (Siam-GNN). We use DropEdge as graph augmentation technique to generate new graphs. Besides, the strategy of constructing Siamese network’s paired inputs is also studied in our work. Notably, stopping gradient backpropagation one side in Siam-GNN is an important factor affecting the performance of model. We equip some graph neural networks with Siamese structure and evaluate these Siam-GNNs on several standard semi-supervised node classification datasets and achieve surprising improvement on almost every original graph neural network.
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Hu, Zhiqiu, Fengjing Shao, and Rencheng Sun. "A New Perspective on Traffic Flow Prediction: A Graph Spatial-Temporal Network with Complex Network Information." Electronics 11, no. 15 (August 4, 2022): 2432. http://dx.doi.org/10.3390/electronics11152432.
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Traffic flow prediction provides support for travel management, vehicle scheduling, and intelligent transportation system construction. In this work, a graph space–time network (GSTNCNI), incorporating complex network feature information, is proposed to predict future highway traffic flow time series. Firstly, a traffic complex network model using traffic big data is established, the topological features of traffic road networks are then analyzed using complex network theory, and finally, the topological features are combined with graph neural networks to explore the roles played by the topological features of 97 traffic network nodes. Consequently, six complex network properties are discussed, namely, degree centrality, clustering coefficient, closeness centrality, betweenness centrality, point intensity, and shortest average path length. This study improves the graph convolutional neural network based on the above six complex network properties and proposes a graph spatial–temporal network consisting of a combination of several complex network properties. By comparison with existing baselines containing graph convolutional neural networks, it is verified that GSTNCNI possesses high traffic flow prediction accuracy and robustness. In addition, ablation experiments are conducted for six different complex network features to verify the effect of different complex network features on the model’s prediction accuracy. Experimental analysis indicates that the model with combined multiple complex network features has a higher prediction accuracy, and its performance is improved by 31.46% on average, compared with the model containing only one complex network feature.
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Zhu, Lingxiao. "E-Commerce Recommendation Algorithm based on Graph Neural Network." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 1264–68. http://dx.doi.org/10.54097/hset.v39i.6752.
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While recommender system is becoming an increasingly essential component in e-commerce websites, although previous models, which directly calculate the similarity of user/item history record, has obtained evidence of effectiveness, recommendations based solely on users' current sequence of actions, when user identity and history preference are not present, has been a popular area due to the growing privacy concerns. This paper demonstrates a model using a graph neural network, which takes the user's sequence of purchasing events as input and constructs a graph derived from it, to make the prediction of the most likely subsequent product that the customer may purchase and make personalized recommendations by the combination of session preference and user’s current interest. Experiments on a real-world e-commerce purchasing event dataset and analysis are carried out to test the model’s performance, as well as how the length of sequence may affect the model preference. The result shows that the model performance has attained a local peak on the dataset used.
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Luo, Hairu, Peng Jia, Anmin Zhou, Yuying Liu, and Ziheng He. "Bridge Node Detection between Communities Based on GNN." Applied Sciences 12, no. 20 (October 13, 2022): 10337. http://dx.doi.org/10.3390/app122010337.
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In a complex network, some nodes are relatively concentrated in topological structure, thus forming a relatively independent node group, which we call a community. Usually, there are multiple communities on a network, and these communities are interconnected and exchange information with each other. A node that plays an important role in the process of information exchange between communities is called an inter-community bridge node. Traditional methods of defining and detecting bridge nodes mostly quantify the bridging effect of nodes by collecting local structural information of nodes and defining index operations. However, on the one hand, it is often difficult to capture the deep topological information in complex networks based on a single indicator, resulting in inaccurate evaluation results; on the other hand, for networks without community structure, such methods may rely on community partitioning algorithms, which require significant computing power. In this paper, considering the multi-dimensional attributes and structural characteristics of nodes, a deep learning-based framework named BND is designed to quickly and accurately detect bridge nodes. Considering that the bridging function of nodes between communities is abstract and complex, and may be related to the multi-dimensional information of nodes, we construct an attribute graph on the basis of the original graph according to the features of the five dimensions of the node to meet our needs for extracting bridging-related attributes. In the deep learning model, we overlay graph neural network layers to process the input attribute graph and add fully connected layers to improve the final classification effect of the model. Graph neural network algorithms including GCN, GAT, and GraphSAGE are compatible with our proposed framework. To the best of our knowledge, our work is the first application of graph neural network techniques in the field of bridge node detection. Experiments show that our designed framework can effectively capture network topology information and accurately detect bridge nodes in the network. In the overall model effect evaluation results based on indicators such as Accuracy and F1 score, our proposed graph neural network model is generally better than baseline methods. In the best case, our model has an Accuracy of 0.9050 and an F1 score of 0.8728.
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Wu, Yi, Naiwang Guo, Binbin Wang, and Lei Zhang. "Research on Situational Awareness Technology of Industrial Control Network Based on Big Data." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012079. http://dx.doi.org/10.1088/1742-6596/2216/1/012079.
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Abstract With the rapid advancement of national strategies such as "Internet+" and "Made in China 2025", industrial control systems have been widely used in various industries such as energy, municipalities, transportation, water conservancy and aerospace, etc. The security of industrial control networks is always affecting the lifeline of the national economy. Therefore, the security of industrial control networks has not been given enough attention, resulting in frequent industrial control network security incidents, which makes us realize the importance of creating an industrial control network situational awareness system that integrates assessment and prediction. In this paper, we study the situational awareness technology of industrial control network based on big data, and integrate situational extraction as the premise, situational assessment as the core, and situational prediction as the goal to comprehensively sense the situational awareness of industrial control network system. Firstly, we adopt two ways of industrial control network data collection in the situational extraction, one is the use of traffic mirroring bypass access to sensor-aware terminals, without affecting the original production services on the premise of network traffic data collection. The second is the use of WireShark tools to achieve industrial control network traffic packet collection and statistics of traffic packets per second, analysis of the current network state, build Hadoop big data platform to achieve offline data pre-processing and feature extraction, and build Flink and TensorFlow model for graph neural network model training and complete prediction. Secondly, we use a combination of hierarchical analysis and correlation analysis to evaluate the situation, and use the evaluation graph to show the current network security state. Thirdly, the situation prediction is done by training the graph neural network model offline. We use the Flink real-time computation engine to read the data of the industrial control network into the graph neural network model in real time, which is used to enhance the feature representation of each node and to predict the probability of anomaly occurrence of the industrial control network in the future period. Finally, the graph neural network model of this paper is compared with traditional neural networks and machine learning models, and the accuracy and false alarm rate indexes are used to demonstrate the high accuracy and robustness of this model.
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Ge, Kao, Jian-Qiang Zhao, and Yan-Yong Zhao. "GR-GNN: Gated Recursion-Based Graph Neural Network Algorithm." Mathematics 10, no. 7 (April 4, 2022): 1171. http://dx.doi.org/10.3390/math10071171.
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Under an internet background involving artificial intelligence and big data—unstructured, materialized, network graph-structured data, such as social networks, knowledge graphs, and compound molecules, have gradually entered into various specific business scenarios. One problem that urgently needs to be solved in the industry involves how to perform feature extractions, transformations, and operations in graph-structured data to solve downstream tasks, such as node classifications and graph classifications in actual business scenarios. Therefore, this paper proposes a gated recursion-based graph neural network (GR-GNN) algorithm to solve tasks such as node depth-dependent feature extractions and node classifications for graph-structured data. The GRU neural network unit was used to complete the node classification task and, thereby, construct the GR-GNN model. In order to verify the accuracy, effectiveness, and superiority of the algorithm on the open datasets Cora, CiteseerX, and PubMed, the algorithm was used to compare the operation results with the classical graph neural network baseline algorithms GCN, GAT, and GraphSAGE, respectively. The experimental results show that, on the validation set, the accuracy and target loss of the GR-GNN algorithm are better than or equal to other baseline algorithms; in terms of algorithm convergence speed, the performance of the GR-GNN algorithm is comparable to that of the GCN algorithm, which is higher than other algorithms. The research results show that the GR-GNN algorithm proposed in this paper has high accuracy and computational efficiency, and very wide application significance.
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Kislyakov, Alexey N. "GRAPH CONVOLUTIONAL NEURAL NETWORKS APPLICATION PERSPECTIVES IN TERRITORIES SPATIAL CLUSTERING." SOFT MEASUREMENTS AND COMPUTING 2, no. 63 (2023): 42–52. http://dx.doi.org/10.36871/2618-9976.2023.02.003.
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The paper considers the actual problem of formation of interregional clusters using spatial data analysis and models based on graph neural networks. The aim is to develop the theoretical and methodological foundations of the application of graph models and deep learning methods for the study of interregional and intermunicipal relationships in the problems of building predictive models of the economic potential of territories. The paper shows the possibility of adapting the typical architecture of spectral graph convolutional network for object clustering. Theoretical foundations for the application of a spectral graph convolutional network for spatial clustering of territories have been formulated and mathematical modeling by the example of a random graph using the tools of opensource libraries networkx, numpy of Python has been performed. The developed approaches are promising for further developments in the construction of recommendation systems in the field of interregional cooperation due to the possibility of taking into account spatial data, as well as socioeconomic indicators of territories in a single model based on the study of network structures.
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Xu, Xiaofeng, Pengcheng Liu, and Mingwu Guo. "Drainage Pattern Recognition of River Network Based on Graph Convolutional Neural Network." ISPRS International Journal of Geo-Information 12, no. 7 (June 21, 2023): 253. http://dx.doi.org/10.3390/ijgi12070253.
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Drainage network pattern recognition is a significant task with wide applications in geographic information mining, map cartography, water resources management, and urban planning. Accurate identification of spatial patterns in river networks can help us understand geographic phenomena, optimize map cartographic quality, assess water resource potential, and provide a scientific basis for urban development planning. However, river network pattern recognition still faces challenges due to the complexity and diversity of river networks. To address this issue, this study proposes a river network pattern recognition method based on graph convolutional networks (GCNs), aiming to achieve accurate classification of different river network patterns. We utilize binary trees to construct a hierarchical tree structure based on river reaches and progressively determine the tree hierarchy by identifying the upstream and downstream relationships among river reaches. Based on this representation, input features for the graph convolutional model are extracted from both spatial and geometric perspectives. The effectiveness of the proposed method is validated through classification experiments on four types of vector river network data (dendritic, fan-shaped, trellis, and fan-shaped). The experimental results demonstrate that the proposed method can effectively classify vector river networks, providing strong support for research and applications in related fields.
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张, 出阳. "Unsupervised Document Clustering Model Based on Topic Model and Graph Neural Network." Computer Science and Application 12, no. 07 (2022): 1795–800. http://dx.doi.org/10.12677/csa.2022.127180.
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Zhang, Jing. "An English Teaching Resource Recommendation System Based on Network Behavior Analysis." Scientific Programming 2021 (November 30, 2021): 1–9. http://dx.doi.org/10.1155/2021/6191543.
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The sharing of English teaching resources has always been a concern. In order to further improve the value of different English teaching resources, this paper proposes a resource management system based on an improved collaborative recommendation algorithm. The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN). The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information. Therefore, the model is constructed by combining GNN and RNN to obtain the advantages of both. In order to prove the effectiveness of the model, we used CNGrid’s real user behavior dataset in the experiment and finally compared the results with other methods. The different deep learning-based models achieved a precision of up to 88% and outperformed other traditional models. The experimental results show that this new deep learning model has good sharing value.
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Li, Dan, and Qian Gao. "Session Recommendation Model Based on Context-Aware and Gated Graph Neural Networks." Computational Intelligence and Neuroscience 2021 (October 13, 2021): 1–10. http://dx.doi.org/10.1155/2021/7266960.
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The graph neural network (GNN) based approach has been successfully applied to session-based recommendation tasks. However, in the face of complex and changing real-world situations, the existing session recommendation algorithms do not fully consider the context information in user decision-making; furthermore, the importance of context information for the behavior model has been widely recognized. Based on this, this paper presents a session recommendation model based on context-aware and gated graph neural networks (CA-GGNNs). First, this paper presents the session sequence as data of graph structure. Second, the embedding vector representation of each item in the session graph is obtained by using the gated graph neural network (GGNN). In this paper, the GRU in GGNN is expanded to replace the input matrix and the state matrix in the conventional GRU with input context captured in the session (e.g., time, location, and holiday) and interval context (representing the proportion of the total session time of each item in the session). Finally, a soft attention mechanism is used to capture users’ interests and preferences, and a recommendation list is given. The CA-GGNN model combines session sequence information with context information at each time. The results on the open Yoochoose and Diginetica datasets show that the model has significantly improved compared with the latest session recommendation methods.
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Feng, Xiao, and Yongdong Xu. "Multi-hop Information-based Graph Convolutional Network for Clustering." Journal of Physics: Conference Series 2555, no. 1 (July 1, 2023): 012012. http://dx.doi.org/10.1088/1742-6596/2555/1/012012.
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Abstract Clustering is an essential and demanding undertaking in data analysis. The combination of traditional neural networks and graph convolutional networks (GCNs) has been extensively discussed in clustering tasks, in which the deep clustering methods learn useful content information and the graph convolutional networks mine the structured neighboring information in the graph data. However, the existing works equally consider the importance of different features to clustering and only focus on the nearest neighboring information in the structured features, ignoring the features of the long distant neighbors in the multi-hop information, resulting in inferior performance. We propose a novel multi-hop information-based graph convolutional network (MIGCN) for clustering to overcome these disadvantages. Specifically, we fuse content features and structured features with adaptive weights, which are dynamically adjusted according to the training results. Meanwhile, we utilize a multi-hop information module to extract structured features. Moreover, we design a multi-supervision mechanism and guide the training and updating of the whole model. Our method achieves better results than previous deep clustering approaches because it takes into account the diverse features embedded in the neural network and a dynamic fusion strategy for clustering. Our model has undergone rigorous testing on widely-accepted standard datasets, employing both objective and subjective evaluation measures. The results consistently demonstrate that our model consistently outperforms state-of-the-art techniques, validating its superior performance.
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Jiang, Ming, and Zhiwei Liu. "Traffic Flow Prediction Based on Dynamic Graph Spatial-Temporal Neural Network." Mathematics 11, no. 11 (May 31, 2023): 2528. http://dx.doi.org/10.3390/math11112528.
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More accurate traffic prediction can further improve the efficiency of intelligent transportation systems. However, the complex spatiotemporal correlation issues in transportation networks pose great challenges. In the past, people have carried out a great deal of research to solve this problem. Most studies are based on graph neural networks to model traffic graphs and attempt to use fixed graph structures to obtain relationships between nodes. However, due to the time-varying spatial correlation of the transportation network, there is no stable node relationship. To address the above issues, we propose a new traffic prediction framework called the Dynamic Graph Spatial-Temporal Neural Network (DGSTN). Unlike other models that use predefined graphs, this model represents stable node relationships and time-varying node relationships by constructing static topology maps and dynamic information maps during the training and testing stages, to capture hidden node relationships and time-varying spatial correlations. In terms of network architecture, we designed multi-scale causal convolution and adaptive spatial self-attention mechanisms to capture temporal and spatial features, respectively, and assisted learning through static and dynamic graphs. The proposed framework has been tested on two real-world traffic datasets and can achieve state-of-the-art performance.
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Xi, Ying, and Xiaohui Cui. "Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength." Entropy 25, no. 5 (May 5, 2023): 754. http://dx.doi.org/10.3390/e25050754.
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Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes. Graph neural networks (GNNs) have emerged as a prominent deep learning architecture, capable of efficiently aggregating node information and discerning node influence. However, existing graph neural networks often ignore the strength of the relationships between nodes when aggregating information about neighboring nodes. In complex networks, neighboring nodes often do not have the same influence on the target node, so the existing graph neural network methods are not effective. In addition, the diversity of complex networks also makes it difficult to adapt node features with a single attribute to different types of networks. To address the above problems, the paper constructs node input features using information entropy combined with the node degree value and the average degree of the neighbor, and proposes a simple and effective graph neural network model. The model obtains the strength of the relationships between nodes by considering the degree of neighborhood overlap, and uses this as the basis for message passing, thereby effectively aggregating information about nodes and their neighborhoods. Experiments are conducted on 12 real networks, using the SIR model to verify the effectiveness of the model with the benchmark method. The experimental results show that the model can identify the influence of nodes in complex networks more effectively.
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Wierciński, Tomasz, Mateusz Rock, Robert Zwierzycki, Teresa Zawadzka, and Michał Zawadzki. "Emotion Recognition from Physiological Channels Using Graph Neural Network." Sensors 22, no. 8 (April 13, 2022): 2980. http://dx.doi.org/10.3390/s22082980.
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In recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The key assumption of the validation was to analyze its correctness for the Circumplex model to further analyze the solution for emotion recognition in the Ekman modal. The novelty of this research is not only the utilization of a GNN network with GraphSleepNet architecture for emotion recognition, but also the analysis of the potential of emotion recognition based on differential entropy features in the Ekman model with a neutral state and a special focus on continuous emotion recognition during the performance of an activity The GNN was validated against the AMIGOS dataset. The research shows how the use of various modalities influences the correctness of the recognition of basic emotions and the neutral state. Moreover, the correctness of the recognition of basic emotions is validated for two configurations of the GNN. The results show numerous interesting observations for Ekman’s model while the accuracy of the Circumplex model is similar to the baseline methods.
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Martin Happ, Matthias Herlich, Christian Maier, Jia Lei Du, and Peter Dorfinger. "Graph-neural-network-based delay estimation for communication networks with heterogeneous scheduling policies." ITU Journal on Future and Evolving Technologies 2, no. 4 (June 25, 2021): 1–8. http://dx.doi.org/10.52953/tejx5530.
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Modeling communication networks to predict performance such as delay and jitter is important for evaluating and optimizing them. In recent years, neural networks have been used to do this, which may have advantages over existing models, for example from queueing theory. One of these neural networks is RouteNet, which is based on graph neural networks. However, it is based on simplified assumptions. One key simplification is the restriction to a single scheduling policy, which describes how packets of different flows are prioritized for transmission. In this paper we propose a solution that supports multiple scheduling policies (Strict Priority, Deficit Round Robin, Weighted Fair Queueing) and can handle mixed scheduling policies in a single communication network. Our solution is based on the RouteNet architecture as part of the "Graph Neural Network Challenge". We achieved a mean absolute percentage error under 1% with our extended model on the evaluation data set from the challenge. This takes neural-network-based delay estimation one step closer to practical use.