Framing a Sustainable Architecture for Data Analytics Systems: An Exploratory Study

Zhao, Jitong; Liu, Yan; Zhou, Peng

doi:10.1109/access.2018.2872673

Cited by 5 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we propose CrimeGAT, a novel application of GATs for predictive policing in criminal networks. Our model integrates attention mechanisms and a sustainable architecture [7] to effectively leverage both node features and graph structure, thereby providing a more accurate and comprehensive prediction of future criminal activity.…”

Section: Introductionmentioning

confidence: 99%

Rumor Detection with Bidirectional Graph Attention Networks

Yang

Wang

2022

Security and Communication Networks

View full text Add to dashboard Cite

In order to extract the relevant features of rumors effectively, this paper proposes a novel rumor detection model with bidirectional graph attention network on the basis of constructing a directed graph, named P-BiGAT. Firstly, this model builds the propagation tree and diffusion tree through the tweet comment and reposting relationship. Secondly, the improved graph attention network (GAT) is used to extract the propagation feature and the diffusion feature through two different directions, and the multihead attention mechanism is used to extract the semantic information of the source tweet. Finally, the propagation feature, diffusion feature, and semantic information representation of the source tweet are connected together through a fully connected layer, and the mapping function is used to determine the authenticity of the information. In addition, this paper also proposes a new node update method and applies it to the model in order to select neighbor node information effectively. Specifically, it can select the neighbor information node with larger weight to update the node according to the weight of the neighbor node. The results of the experiment show that the model is better than the baseline method of comparison in accuracy, precision, recall, and F1 measure on the public datasets.

show abstract

Section: Introductionmentioning

confidence: 99%

Rumor Detection with Bidirectional Graph Attention Networks

Yang

Wang

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…In this paper, we introduce CrimeGraphNet, a novel framework that leverages the power of GCNs and a sustainable architecture [7] for predicting hidden links in criminal networks. Our approach extends traditional GCN models by incorporating several enhancements tailored specifically to the unique challenges presented by criminal networks, including…”

Section: Introductionmentioning

confidence: 99%

Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision

Yang

Sun

et al. 2023

Neural Networks

View full text Add to dashboard Cite

“…In this paper, we introduce CrimeGNN and a sustainable architecture [7], a novel application of GNNs designed to detect communities within criminal networks. CrimeGNN ingests a graph where vertices represent individuals in a criminal network and edges represent relationships between…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks in Anomaly Detection

Wang

2022

Graph Neural Networks: Foundations, Frontiers, and Applications

View full text Add to dashboard Cite

Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first step towards analyzing anomalies via the lens of the graph spectrum. Our crucial observation is the existence of anomalies will lead to the 'rightshift' phenomenon, that is, the spectral energy distribution concentrates less on low frequencies and more on high frequencies. This fact motivates us to propose the Beta Wavelet Graph Neural Network (BWGNN). Indeed, BWGNN has spectral and spatial localized band-pass filters to better handle the 'right-shift' phenomenon in anomalies. We demonstrate the effectiveness of BWGNN on four large-scale anomaly detection datasets. Our code and data are released at https://github.com/squareRoot3/ Rethinking-Anomaly-Detection.

show abstract

Framing a Sustainable Architecture for Data Analytics Systems: An Exploratory Study

Cited by 5 publications

References 36 publications

Rumor Detection with Bidirectional Graph Attention Networks

Rumor Detection with Bidirectional Graph Attention Networks

Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision

Graph Neural Networks in Anomaly Detection

Contact Info

Product

Resources

About