scholarly journals K-Core based Temporal Graph Convolutional Network for Dynamic Graphs

2020 ◽  
pp. 1-1
Author(s):  
Jingxin Liu ◽  
Chang Xu ◽  
Chang Yin ◽  
Weiqiang Wu ◽  
You Song
Keyword(s):  
Dynamic Graphs ◽  
Convolutional Network ◽  
Temporal Graph

scholarly journals Cyber—Physical Attack Detection in Water Distribution Systems with Temporal Graph Convolutional Neural Networks

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1247
Author(s):  
Lydia Tsiami ◽  
Christos Makropoulos
Keyword(s):  
Neural Networks ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Attack Detection ◽  
Convolutional Network ◽  
Irreversible Damage ◽  
Physical Attack ◽  
Temporal Graph ◽  
Graph Neural Networks

Prompt detection of cyber–physical attacks (CPAs) on a water distribution system (WDS) is critical to avoid irreversible damage to the network infrastructure and disruption of water services. However, the complex interdependencies of the water network’s components make CPA detection challenging. To better capture the spatiotemporal dimensions of these interdependencies, we represented the WDS as a mathematical graph and approached the problem by utilizing graph neural networks. We presented an online, one-stage, prediction-based algorithm that implements the temporal graph convolutional network and makes use of the Mahalanobis distance. The algorithm exhibited strong detection performance and was capable of localizing the targeted network components for several benchmark attacks. We suggested that an important property of the proposed algorithm was its explainability, which allowed the extraction of useful information about how the model works and as such it is a step towards the creation of trustworthy AI algorithms for water applications. Additional insights into metrics commonly used to rank algorithm performance were also presented and discussed.

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

scholarly journals STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

2020 ◽  
Vol 34 (02) ◽  
pp. 1342-1350 ◽  
Author(s):  
Uttaran Bhattacharya ◽  
Trisha Mittal ◽  
Rohan Chandra ◽  
Tanmay Randhavane ◽  
Aniket Bera ◽  
...  
Keyword(s):  
Real World ◽  
Classification Accuracy ◽  
Emotion Perception ◽  
Convolutional Network ◽  
Human Emotion ◽  
Convolutional Networks ◽  
Variational Autoencoder ◽  
Gait Features ◽  
Temporal Graph ◽  
Perceived Emotion

We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Given an RGB video of an individual walking, our formulation implicitly exploits the gait features to classify the perceived emotion of the human into one of four emotions: happy, sad, angry, or neutral. We train STEP on annotated real-world gait videos, augmented with annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE). We incorporate a novel push-pull regularization loss in the CVAE formulation of STEP-Gen to generate realistic gaits and improve the classification accuracy of STEP. We also release a novel dataset (E-Gait), which consists of 4,227 human gaits annotated with perceived emotions along with thousands of synthetic gaits. In practice, STEP can learn the affective features and exhibits classification accuracy of 88% on E-Gait, which is 14–30% more accurate over prior methods.

scholarly journals DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 11924-11931
Author(s):  
Zhongwei Qiu ◽  
Kai Qiu ◽  
Jianlong Fu ◽  
Dongmei Fu
Keyword(s):  
Pose Estimation ◽  
State Of The Art ◽  
Semantic Relations ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Convolutional Network ◽  
Bottom Up ◽  
Multi Level ◽  
Human Pose ◽  
Relative Gains

Multi-person pose estimation aims to detect human keypoints from images with multiple persons. Bottom-up methods for multi-person pose estimation have attracted extensive attention, owing to the good balance between efficiency and accuracy. Recent bottom-up methods usually follow the principle of keypoints localization and grouping, where relations between keypoints are the keys to group keypoints. These relations spontaneously construct a graph of keypoints, where the edges represent the relations between two nodes (i.e., keypoints). Existing bottom-up methods mainly define relations by empirically picking out edges from this graph, while omitting edges that may contain useful semantic relations. In this paper, we propose a novel Dynamic Graph Convolutional Module (DGCM) to model rich relations in the keypoints graph. Specifically, we take into account all relations (all edges of the graph) and construct dynamic graphs to tolerate large variations of human pose. The DGCM is quite lightweight, which allows it to be stacked like a pyramid architecture and learn structural relations from multi-level features. Our network with single DGCM based on ResNet-50 achieves relative gains of 3.2% and 4.8% over state-of-the-art bottom-up methods on COCO keypoints and MPII dataset, respectively.

scholarly journals A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

2021 ◽  
Vol 10 (7) ◽  
pp. 485
Author(s):  
Jiandong Bai ◽  
Jiawei Zhu ◽  
Yujiao Song ◽  
Ling Zhao ◽  
Zhixiang Hou ◽  
...  
Keyword(s):  
Road Network ◽  
Temporal Dynamics ◽  
Technological Problem ◽  
Traffic Flows ◽  
Convolutional Network ◽  
Temporal Dimension ◽  
Time Points ◽  
The Road ◽  
Traffic Forecasting ◽  
Temporal Graph

Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In the spatial dimension, due to the connectivity of the road network, the traffic flows between linked roads are closely related. In the temporal dimension, although there exists a tendency among adjacent time points, the importance of distant time points is not necessarily less than that of recent ones, since traffic flows are also affected by external factors. In this study, an attention temporal graph convolutional network (A3T-GCN) was proposed to simultaneously capture global temporal dynamics and spatial correlations in traffic flows. The A3T-GCN model learns the short-term trend by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional network. Moreover, the attention mechanism was introduced to adjust the importance of different time points and assemble global temporal information to improve prediction accuracy. Experimental results in real-world datasets demonstrate the effectiveness and robustness of the proposed A3T-GCN. We observe the improvements in RMSE of 2.51–46.15% and 2.45–49.32% over baselines for the SZ-taxi and Los-loop, respectively. Meanwhile, the Accuracies are 0.95–89.91% and 0.26–10.37% higher than the baselines for the SZ-taxi and Los-loop, respectively.

scholarly journals Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 1509 ◽  
Author(s):  
Liang Ge ◽  
Siyu Li ◽  
Yaqian Wang ◽  
Feng Chang ◽  
Kunyan Wu
Keyword(s):  
Weather Conditions ◽  
Urban Traffic ◽  
Traffic Data ◽  
Convolutional Network ◽  
Temporal Dimension ◽  
Temporal Correlations ◽  
Speed Prediction ◽  
External Component ◽  
Temporal Graph ◽  
Traffic Speed

Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However, due to the complex spatial-temporal correlations of traffic data, it is very challenging to predict traffic speed timely and accurately. The traffic speed renders not only short-term neighboring and multiple long-term periodic dependencies in the temporal dimension but also local and global dependencies in the spatial dimension. To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction. The model consists of three spatial-temporal components with the same structure and an external component. The three spatial-temporal components are used to model the recent, daily-periodic, and weekly-periodic spatial-temporal correlations of the traffic data, respectively. More specifically, each spatial-temporal component consists of a dynamic temporal module and a global correlated spatial module. The former contains multiple residual blocks which are stacked by dilated casual convolutions, while the latter contains a localized graph convolution and a global correlated mechanism. The external component is used to extract the effect of external factors, such as holidays and weather conditions, on the traffic speed. Experimental results on two real-world traffic datasets have demonstrated that the proposed GSTGCN outperforms the state-of-the-art baselines.

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