Hybrid deep graph convolutional networks

Yang, Fei; Zhang, Huyin; Tao, Shiming

doi:10.1007/s13042-022-01520-y

Cited by 14 publications

(11 citation statements)

References 20 publications

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“…Several embedding methods have been proposed in the literature for modelling road networks. In [ 12 ], a hybrid graph convolution neural network (HGCN) method is proposed for traffic flow prediction in highway networks, where nodes represent the toll stations while edges represent road segments between two toll stations. In addition to modelling the spatial feature of the highway, the authors achieved better traffic flow prediction by considering factors such as time, space, weather conditions, and data type of each toll station.…”

Section: Background and Related Workmentioning

confidence: 99%

“…It is worth noting that the HGCN method proposed in [ 12 ] uses local neighbourhood aggregation to learn the spatial connection of toll stations, and it cannot integrate road segment features into the learning process. This is valid since many state-of-the-art GRL methods rely on node features only.…”

Section: Background and Related Workmentioning

confidence: 99%

“…( 10) Obtain the updated weight parameters using equation ( 5). ( 11) end while (12) Store optimal weight parameters W Opt . ( 13 Computational Intelligence and Neuroscience where they can be separated linearly.…”

Section: Support Vector Machinesmentioning

confidence: 99%

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Road‐Type Classification with Deep AutoEncoder

Molefe

Tapamo

2023

Computational Intelligence and Neuroscience

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Machine learning algorithms are among the driving forces towards the success of intelligent road network systems design. Such algorithms allow for the design of systems that provide safe road usage, efficient infrastructure, and traffic flow management. One such application of machine learning in intelligent road networks is classifying different road network types that provide useful traffic information to road users. We propose a deep autoencoder model for representation learning to classify road network types. Each road segment node is represented as a feature vector. Unlike existing graph embedding methods that perform road segment embedding using the neighbouring road segments, the proposed method performs embedding directly on the road segment vectors. The proposed method performs embedding directly on the road segment vectors. Comparison with state-of-the-art graph embedding methods show that the proposed method outperforms graph convolution networks, GraphSAGE-MEAN, graph attention networks, and graph isomorphism network methods, and it achieves similar performance to GraphSAGE-MAXPOOL.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

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Road‐Type Classification with Deep AutoEncoder

Molefe

Tapamo

2023

Computational Intelligence and Neuroscience

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“…Due to the preference factor k, we should only use the news related to user u. 32 Formally, there is uncertainty when users click on the news. We set the uncertainty to r n;k and suppose that it represents the probability that user u will click on news n due to preference factor k. Then, to generate u k , we should also employ n. The undetermined potential variable r n;k will be deduced during the loop.…”

Section: Return Y Umentioning

confidence: 99%

Graph neural network news recommendation based on weight learning and preference decomposition

Lu,

2023

J. Electron. Imag.

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Personalized news suggestions are an important technology to enhance people's online news reading experiences. How to better understand users and news representation is a major issue in news recommendation. The majority of cutting-edge news recommendation techniques mostly neglect the link between title and content, explicitly and implicitly. They neglect to take into account the effects of many prospective news preferences on people's behavior when they click on various news items. We first build a user-news interaction graph and then present the weight learning and preference decomposition (WLPD) news recommendation model for graph neural networks, which is based on WLPD. This model not only takes into account the impact of the relationship between news titles and content, explicit and implicit, on the likelihood that users will click on the news, but also takes into account the various potential preferences between users and news interaction. Finally, using actual news databases, we run a number of experiments. We discover that our model significantly improved in terms of accuracy and performance compared with other cutting-edge news recommendation techniques.

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“…Low-level features usually contain information, such as object boundaries, vertices, and textures, while high-level features are usually abstract but informative. 32 This top-down feature structure enables the network to have efficient representation capabilities. 33 However, since the receptive field sharing the convolution kernel is determined at the initial stage of the algorithm design, it will not change with model training, and it is easy to ignore the relationship between the whole and the part, which is not conducive to grasping the context features.…”

Section: Introductionmentioning

confidence: 99%

Optimized anchor-free network for dense rotating object detection in remote sensing images

Yan,

Zhang,

Hong

et al. 2023

J. Electron. Imag.

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Extracting dense rotating objects accurately from remote sensing images is an emerging task in object detection. To increase the applicability of existing algorithms in the above tasks, an optimized anchor-free network optimized by a dual attention mechanism (DAM) and gate multiscale feature fusion (GMFF) is designed. The DAM module is composed of two attention mechanisms with different functions. This part can enhance the backbone network's ability to extract and model information at different levels and reduce the accuracy loss caused by object density changes in the image. The GMFF module uses the gating structure to realize adaptive transmission and integration of multiscale information. Through this module, the useless information in features will be filtered, and the key information will be retained. Several experiments are designed to verify the feasibility of the algorithm. Compared with the baseline model, adding DAM and GMFF to the dense rotating object extraction task in remote sensing images improves the model accuracy by 3.5% and 2.1%, respectively, while adding two modules simultaneously, and the accuracy increases from 79.1% to 84.3%. In conventional object extraction tasks, such as dataset for object detection in aerial images and HRSC2016, our method has the highest accuracy compared to other similar algorithms, with 76.5% and 90.3%, respectively.

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Hybrid deep graph convolutional networks

Cited by 14 publications

References 20 publications

Road‐Type Classification with Deep AutoEncoder

Road‐Type Classification with Deep AutoEncoder

Graph neural network news recommendation based on weight learning and preference decomposition

Optimized anchor-free network for dense rotating object detection in remote sensing images

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