Weigang Li

Orcid: 0000-0001-8290-2275

Affiliations:
  • Northwestern Polytechnical University, School of Software, Xi'an, China


According to our database1, Weigang Li authored at least 12 papers between 2020 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Graph structure learning with joint node and structural feature representation for node classification.
Neurocomputing, 2026

Quantum-resistant blockchain architecture for secure vehicular networks: A ML-KEM-enabled approach with PoA and PoP consensus.
Future Gener. Comput. Syst., 2026

2025
Microservice Call Chain Anomaly Detection and Root Cause Localization Method Based on Graph Neural Networks.
Proceedings of the 12th International Conference on Dependable Systems and Their Applications, 2025

Fault Localization Algorithm Based on Multimodal Contrastive and Causal Learning.
Proceedings of the 12th International Conference on Dependable Systems and Their Applications, 2025

2024
Hierarchical aggregation perceptual pipeline for tactical intention recognition.
Multim. Tools Appl., June, 2024

DWOSC: Dynamic Weight Optimization and Smoothness Constraint for Sensor-Based Human Activity Recognition.
IEEE Trans. Instrum. Meas., 2024

2023
Quality and content-aware fusion optimization mechanism of infrared and visible images.
Multim. Tools Appl., December, 2023

Intrusion Detection using hybridized Meta-heuristic techniques with Weighted XGBoost Classifier.
Expert Syst. Appl., December, 2023

Denoising Aggregation of Graph Neural Networks by Using Principal Component Analysis.
IEEE Trans. Ind. Informatics, March, 2023

Temporal-Spatial Dynamic Convolutional Neural Network for Human Activity Recognition Using Wearable Sensors.
IEEE Trans. Instrum. Meas., 2023

2021
MobileGCN applied to low-dimensional node feature learning.
Pattern Recognit., 2021

2020
Design of affinity-aware encoding by embedding graph centrality for graph classification.
Neurocomputing, 2020


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