SHAP dependency graph. by Jinlin Teng (17972595)

An illustration of the Deepwalk algorithm operation involves a random walk on the graph, resulting in a “walk” sequence, which is then utilized to construct a graph. by Xiaobing Deng (1622233)

Comparison of Traffic Accident Risk Prediction Models Based on Different Algorithms. by Hao Li (31608)

Graph of YOLOv8n detection results with different loss functions. by Wenyuan Xu (2553028)

Multi-scale time convolution component. by Kang Xu (708915)

“…To address these issues, this paper proposes a non-end-to-end adaptive graph learning algorithm capable of effectively capturing complex dependencies. …”

The statistics of various datasets. by Kang Xu (708915)

“…To address these issues, this paper proposes a non-end-to-end adaptive graph learning algorithm capable of effectively capturing complex dependencies. …”

Traffic incidents on highways. by Kang Xu (708915)

“…To address these issues, this paper proposes a non-end-to-end adaptive graph learning algorithm capable of effectively capturing complex dependencies. …”

Model architecture overview. by Kang Xu (708915)

“…To address these issues, this paper proposes a non-end-to-end adaptive graph learning algorithm capable of effectively capturing complex dependencies. …”

Multi-scale temporal attention component. by Kang Xu (708915)

“…To address these issues, this paper proposes a non-end-to-end adaptive graph learning algorithm capable of effectively capturing complex dependencies. …”

Traffic flow chart of Node 10 and Node 30. by Kang Xu (708915)

“…To address these issues, this paper proposes a non-end-to-end adaptive graph learning algorithm capable of effectively capturing complex dependencies. …”

Ablation experiments on PeMSD4 and PeMSD8. by Kang Xu (708915)

“…To address these issues, this paper proposes a non-end-to-end adaptive graph learning algorithm capable of effectively capturing complex dependencies. …”

Flowchart of the GAN–BWGNN HAD algorithm. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

<b>Research on Semantic Segmentation of PCB Point Clouds Based on Adaptive Dynamic Graph Convolution</b> by zedong huang (22221292)

<b>Integrating Deep Learning and Superpixel-based Graph Convolution for Ecological Constraint Calibration in Urban Growth Modeling: The ANN-SGCCA Approach</b> by Anonymous Anonymous (12685409)

“…However, existing urban growth simulation studies have the following limitations: 1) the heterogeneous neighborhood spatial interactions of ecological factors at multiple scales are often overlooked; 2) deep learning algorithms are less used to learn the characteristics of these complex interactions and capture ecological constraints. …”

Background-anomaly separation in Cri dataset. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

Comparison of running time (seconds). by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

Comparison of AUC values. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

Influence of C on the AUC on the seven datasets. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

AUC for ablation study. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

Influence of K on the AUC on the seven datasets. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”