Dual-Attention Graph Convolutional Network

Zhang, Xueya; Zhang, Tong; Zhao, Wenting; Cui, Zhen; Yang, Jian

doi:10.1007/978-3-030-41299-9_19

Cited by 11 publications

(8 citation statements)

References 20 publications

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“…2 the top-1 classification accuracy on both settings, comparing it with other existing approaches. As illustrated, CCP outperforms previous state-of-the-art methods on this dataset, including other graph-oriented architectures[31,17], despite being more general and not specifically designed to only address action recognition settings.…”

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confidence: 82%

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Classifying Signals on Irregular Domains via Convolutional Cluster Pooling

Porrello¹,

Abati²,

Calderara³

et al. 2019

Preprint

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We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset. To this end, we introduce a Convolutional Cluster Pooling layer exploiting a multi-scale clustering in order to highlight, at different resolutions, locally connected regions on the input graph. Our proposal generalises well-established neural models such as Convolutional Neural Networks (CNNs) on irregular and complex domains, by means of the exploitation of the weight sharing property in a graph-oriented architecture. In this work, such property is based on the centrality of each vertex within its soft-assigned cluster. Extensive experiments on NTU RGB+D, CIFAR-10 and 20NEWS demonstrate the effectiveness of the proposed technique in capturing both local and global patterns in graph-structured data out of different domains.

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confidence: 82%

“…Lie Group [28] 50.1 52.8 HBRNN-L [5] 59.1 64.0 P-LSTM [24] 62.9 70.3 ST-LSTM+TS [19] 69.2 77.7 TGCNN [31] 71.4 82.9 Temporal Conv [12] 74.3 83.1 Deep STGC K [17] 74.9 86.3 C-CNN + MTLN [11] 79.6 84.8 CCP (our) 80.1 86.8…”

Section: Methods Cross Subject Cross Viewmentioning

confidence: 99%

Classifying Signals on Irregular Domains via Convolutional Cluster Pooling

Porrello¹,

Abati²,

Calderara³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Generally speaking, words that frequently appear together can express a specific context and reflect a specific event, which helps us to detect fake news. We collect word co-occurrence information through the sliding window, and calculate the weight by a popular measure of word association, point-wise mutual information (PMI) [26]. E ru reflects the rumor-user and user-user interactions, and describes the structural relationship between rumor propagation and diffusion.…”

Section: Multi-information Heterogeneous Graph Constructionmentioning

confidence: 99%

Jointly multi-source information and local-global relations of heterogeneous network for rumor detection

Han

Zhao²,

Zhang³

et al. 2023

Front. Phys.

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The widespread rumors on social media seriously disturb the social order, and we urgently need practical methods to detect rumors. Most existing deep learning methods focus on mining news text content, user information, and propagation features but ignore the rumor diffusion structural features. Rumors spread in a vertical chain and diffusion in a horizontal network. Both are essential features of rumors. In addition, existing models need more effective methods to extract higher-order features of multiple resource information. To address these problems, we propose a multi-source information heterogeneous graph model in this paper, called jointly Multi-Source information and Local-Global relationship of heterogeneous network model named MSLG. It extracts multi-source information such as rumors content, user information, propagation, and diffusion structure. Firstly, we extract the higher order semantic representation of rumors content by graph convolution network and integrate local relational attention to strengthen the critical semantic. At the same time, we construct the rumors and users as heterogeneous graphs to capture the propagation and diffusion structure of the rumors. We are finally fusing global relational attention to measure submodules’ importance. Experiments on two real-world datasets show that the proposed method achieves state-of-the-art results in fake news detection.

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“…Recently, lots of researchers have studies dual-attention graph neural networks [ 37 , 38 ] and developed a serious of application for general spatio-temporal network in different urban traffic scene [ 39 , 40 ].…”

Section: Related Workmentioning

confidence: 99%

SGDAN—A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction

Mei

Liu

Pan

et al. 2020

Sensors

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There has been a lot of research on flight delays. But it is more useful and difficult to estimate the departure delay time especially three hours before the scheduled time of departure, from which passengers can reasonably plan their travel time and the airline and airport staff can schedule flights more reasonably. In this paper, we develop a Spatio-temporal Graph Dual-Attention Neural Network (SGDAN) to learn the departure delay time for each flight with real-time conditions at three hours before the scheduled time of departure. Specifically, it first models the air traffic network as graph sequences, what is, using a heterogeneous graph to model a flight and its adjacent flights with the same departure or arrival airport in a special time interval, and using a sequence to model the flight and its previous flights that share the same aircraft. The main contributions of this paper are using heterogeneous graph-level attention to learn the influence between the flight and its adjacent flight together with sequence-level attention to learn the influence between the flight and its previous flight in the flight sequence. With aggregating features from the learned influence from both graph-level and sequence-level attention, SGDAN can generate node embedding to estimate the departure delay time. Experiments on a real-world large-scale data set show that SGDAN produces better results than state-of-the-art models in the accurate flight delay time estimation task.

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Dual-Attention Graph Convolutional Network

Cited by 11 publications

References 20 publications

Classifying Signals on Irregular Domains via Convolutional Cluster Pooling

Classifying Signals on Irregular Domains via Convolutional Cluster Pooling

Jointly multi-source information and local-global relations of heterogeneous network for rumor detection

SGDAN—A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction

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